import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression, Lasso, Ridge
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.feature_selection import SequentialFeatureSelector
from scipy import stats
from sklearn.preprocessing import StandardScaler,MinMaxScaler
from statsmodels.stats.outliers_influence import variance_inflation_factor
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', 100)
df = pd.read_excel("Attrition Case Study.xlsx")
df.head()
| Attrition | Age | BusinessTravel | DailyRate | Department | DistanceFromHome | Education | EducationField | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | Gender | HourlyRate | JobInvolvement | JobLevel | JobRole | JobSatisfaction | MaritalStatus | MonthlyIncome | MonthlyRate | NumCompaniesWorked | Over18 | OverTime | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 41 | Travel_Rarely | 1102 | Sales | 1 | 2 | Life Sciences | 1 | 1 | 2 | Female | 94 | 3 | 2 | Sales Executive | 4 | Single | 5993 | 19479 | 8 | Y | Yes | 11 | 3 | 1 | 80 | 0 | 8 | 0 | 1 | 6 | 4 | 0 | 5 |
| 1 | 0 | 49 | Travel_Frequently | 279 | Research & Development | 8 | 1 | Life Sciences | 1 | 2 | 3 | Male | 61 | 2 | 2 | Research Scientist | 2 | Married | 5130 | 24907 | 1 | Y | No | 23 | 4 | 4 | 80 | 1 | 10 | 3 | 3 | 10 | 7 | 1 | 7 |
| 2 | 1 | 37 | Travel_Rarely | 1373 | Research & Development | 2 | 2 | Other | 1 | 4 | 4 | Male | 92 | 2 | 1 | Laboratory Technician | 3 | Single | 2090 | 2396 | 6 | Y | Yes | 15 | 3 | 2 | 80 | 0 | 7 | 3 | 3 | 0 | 0 | 0 | 0 |
| 3 | 0 | 33 | Travel_Frequently | 1392 | Research & Development | 3 | 4 | Life Sciences | 1 | 5 | 4 | Female | 56 | 3 | 1 | Research Scientist | 3 | Married | 2909 | 23159 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 8 | 3 | 3 | 8 | 7 | 3 | 0 |
| 4 | 0 | 27 | Travel_Rarely | 591 | Research & Development | 2 | 1 | Medical | 1 | 7 | 1 | Male | 40 | 3 | 1 | Laboratory Technician | 2 | Married | 3468 | 16632 | 9 | Y | No | 12 | 3 | 4 | 80 | 1 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
df.shape
(1470, 35)
df.columns
Index(['Attrition', 'Age', 'BusinessTravel', 'DailyRate', 'Department',
'DistanceFromHome', 'Education', 'EducationField', 'EmployeeCount',
'EmployeeNumber', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',
'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',
'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',
'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',
'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',
'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',
'YearsWithCurrManager'],
dtype='object')
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1470 entries, 0 to 1469 Data columns (total 35 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Attrition 1470 non-null int64 1 Age 1470 non-null int64 2 BusinessTravel 1470 non-null object 3 DailyRate 1470 non-null int64 4 Department 1470 non-null object 5 DistanceFromHome 1470 non-null int64 6 Education 1470 non-null int64 7 EducationField 1470 non-null object 8 EmployeeCount 1470 non-null int64 9 EmployeeNumber 1470 non-null int64 10 EnvironmentSatisfaction 1470 non-null int64 11 Gender 1470 non-null object 12 HourlyRate 1470 non-null int64 13 JobInvolvement 1470 non-null int64 14 JobLevel 1470 non-null int64 15 JobRole 1470 non-null object 16 JobSatisfaction 1470 non-null int64 17 MaritalStatus 1470 non-null object 18 MonthlyIncome 1470 non-null int64 19 MonthlyRate 1470 non-null int64 20 NumCompaniesWorked 1470 non-null int64 21 Over18 1470 non-null object 22 OverTime 1470 non-null object 23 PercentSalaryHike 1470 non-null int64 24 PerformanceRating 1470 non-null int64 25 RelationshipSatisfaction 1470 non-null int64 26 StandardHours 1470 non-null int64 27 StockOptionLevel 1470 non-null int64 28 TotalWorkingYears 1470 non-null int64 29 TrainingTimesLastYear 1470 non-null int64 30 WorkLifeBalance 1470 non-null int64 31 YearsAtCompany 1470 non-null int64 32 YearsInCurrentRole 1470 non-null int64 33 YearsSinceLastPromotion 1470 non-null int64 34 YearsWithCurrManager 1470 non-null int64 dtypes: int64(27), object(8) memory usage: 402.1+ KB
df.isna().sum()
Attrition 0 Age 0 BusinessTravel 0 DailyRate 0 Department 0 DistanceFromHome 0 Education 0 EducationField 0 EmployeeCount 0 EmployeeNumber 0 EnvironmentSatisfaction 0 Gender 0 HourlyRate 0 JobInvolvement 0 JobLevel 0 JobRole 0 JobSatisfaction 0 MaritalStatus 0 MonthlyIncome 0 MonthlyRate 0 NumCompaniesWorked 0 Over18 0 OverTime 0 PercentSalaryHike 0 PerformanceRating 0 RelationshipSatisfaction 0 StandardHours 0 StockOptionLevel 0 TotalWorkingYears 0 TrainingTimesLastYear 0 WorkLifeBalance 0 YearsAtCompany 0 YearsInCurrentRole 0 YearsSinceLastPromotion 0 YearsWithCurrManager 0 dtype: int64
new_df= df.drop(["Attrition"],axis=1)
new_df.head()
| Age | BusinessTravel | DailyRate | Department | DistanceFromHome | Education | EducationField | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | Gender | HourlyRate | JobInvolvement | JobLevel | JobRole | JobSatisfaction | MaritalStatus | MonthlyIncome | MonthlyRate | NumCompaniesWorked | Over18 | OverTime | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 41 | Travel_Rarely | 1102 | Sales | 1 | 2 | Life Sciences | 1 | 1 | 2 | Female | 94 | 3 | 2 | Sales Executive | 4 | Single | 5993 | 19479 | 8 | Y | Yes | 11 | 3 | 1 | 80 | 0 | 8 | 0 | 1 | 6 | 4 | 0 | 5 |
| 1 | 49 | Travel_Frequently | 279 | Research & Development | 8 | 1 | Life Sciences | 1 | 2 | 3 | Male | 61 | 2 | 2 | Research Scientist | 2 | Married | 5130 | 24907 | 1 | Y | No | 23 | 4 | 4 | 80 | 1 | 10 | 3 | 3 | 10 | 7 | 1 | 7 |
| 2 | 37 | Travel_Rarely | 1373 | Research & Development | 2 | 2 | Other | 1 | 4 | 4 | Male | 92 | 2 | 1 | Laboratory Technician | 3 | Single | 2090 | 2396 | 6 | Y | Yes | 15 | 3 | 2 | 80 | 0 | 7 | 3 | 3 | 0 | 0 | 0 | 0 |
| 3 | 33 | Travel_Frequently | 1392 | Research & Development | 3 | 4 | Life Sciences | 1 | 5 | 4 | Female | 56 | 3 | 1 | Research Scientist | 3 | Married | 2909 | 23159 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 8 | 3 | 3 | 8 | 7 | 3 | 0 |
| 4 | 27 | Travel_Rarely | 591 | Research & Development | 2 | 1 | Medical | 1 | 7 | 1 | Male | 40 | 3 | 1 | Laboratory Technician | 2 | Married | 3468 | 16632 | 9 | Y | No | 12 | 3 | 4 | 80 | 1 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
new_df.shape
(1470, 34)
cat = []
con = []
for i in new_df.columns:
if(df[i].dtypes == "object"):
cat.append(i)
else:
con.append(i)
cat,con
(['BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole', 'MaritalStatus', 'Over18', 'OverTime'], ['Age', 'DailyRate', 'DistanceFromHome', 'Education', 'EmployeeCount', 'EmployeeNumber', 'EnvironmentSatisfaction', 'HourlyRate', 'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager'])
df_cat = new_df[cat]
df_cat
| BusinessTravel | Department | EducationField | Gender | JobRole | MaritalStatus | Over18 | OverTime | |
|---|---|---|---|---|---|---|---|---|
| 0 | Travel_Rarely | Sales | Life Sciences | Female | Sales Executive | Single | Y | Yes |
| 1 | Travel_Frequently | Research & Development | Life Sciences | Male | Research Scientist | Married | Y | No |
| 2 | Travel_Rarely | Research & Development | Other | Male | Laboratory Technician | Single | Y | Yes |
| 3 | Travel_Frequently | Research & Development | Life Sciences | Female | Research Scientist | Married | Y | Yes |
| 4 | Travel_Rarely | Research & Development | Medical | Male | Laboratory Technician | Married | Y | No |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | Travel_Frequently | Research & Development | Medical | Male | Laboratory Technician | Married | Y | No |
| 1466 | Travel_Rarely | Research & Development | Medical | Male | Healthcare Representative | Married | Y | No |
| 1467 | Travel_Rarely | Research & Development | Life Sciences | Male | Manufacturing Director | Married | Y | Yes |
| 1468 | Travel_Frequently | Sales | Medical | Male | Sales Executive | Married | Y | No |
| 1469 | Travel_Rarely | Research & Development | Medical | Male | Laboratory Technician | Married | Y | No |
1470 rows × 8 columns
df_con = new_df[con]
df_con
| Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 41 | 1102 | 1 | 2 | 1 | 1 | 2 | 94 | 3 | 2 | 4 | 5993 | 19479 | 8 | 11 | 3 | 1 | 80 | 0 | 8 | 0 | 1 | 6 | 4 | 0 | 5 |
| 1 | 49 | 279 | 8 | 1 | 1 | 2 | 3 | 61 | 2 | 2 | 2 | 5130 | 24907 | 1 | 23 | 4 | 4 | 80 | 1 | 10 | 3 | 3 | 10 | 7 | 1 | 7 |
| 2 | 37 | 1373 | 2 | 2 | 1 | 4 | 4 | 92 | 2 | 1 | 3 | 2090 | 2396 | 6 | 15 | 3 | 2 | 80 | 0 | 7 | 3 | 3 | 0 | 0 | 0 | 0 |
| 3 | 33 | 1392 | 3 | 4 | 1 | 5 | 4 | 56 | 3 | 1 | 3 | 2909 | 23159 | 1 | 11 | 3 | 3 | 80 | 0 | 8 | 3 | 3 | 8 | 7 | 3 | 0 |
| 4 | 27 | 591 | 2 | 1 | 1 | 7 | 1 | 40 | 3 | 1 | 2 | 3468 | 16632 | 9 | 12 | 3 | 4 | 80 | 1 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 36 | 884 | 23 | 2 | 1 | 2061 | 3 | 41 | 4 | 2 | 4 | 2571 | 12290 | 4 | 17 | 3 | 3 | 80 | 1 | 17 | 3 | 3 | 5 | 2 | 0 | 3 |
| 1466 | 39 | 613 | 6 | 1 | 1 | 2062 | 4 | 42 | 2 | 3 | 1 | 9991 | 21457 | 4 | 15 | 3 | 1 | 80 | 1 | 9 | 5 | 3 | 7 | 7 | 1 | 7 |
| 1467 | 27 | 155 | 4 | 3 | 1 | 2064 | 2 | 87 | 4 | 2 | 2 | 6142 | 5174 | 1 | 20 | 4 | 2 | 80 | 1 | 6 | 0 | 3 | 6 | 2 | 0 | 3 |
| 1468 | 49 | 1023 | 2 | 3 | 1 | 2065 | 4 | 63 | 2 | 2 | 2 | 5390 | 13243 | 2 | 14 | 3 | 4 | 80 | 0 | 17 | 3 | 2 | 9 | 6 | 0 | 8 |
| 1469 | 34 | 628 | 8 | 3 | 1 | 2068 | 2 | 82 | 4 | 2 | 3 | 4404 | 10228 | 2 | 12 | 3 | 1 | 80 | 0 | 6 | 3 | 4 | 4 | 3 | 1 | 2 |
1470 rows × 26 columns
#we check and handle outliers in continuous features only
df_con.boxplot()
<AxesSubplot:>
df_con.columns
Index(['Age', 'DailyRate', 'DistanceFromHome', 'Education', 'EmployeeCount',
'EmployeeNumber', 'EnvironmentSatisfaction', 'HourlyRate',
'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome',
'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike',
'PerformanceRating', 'RelationshipSatisfaction', 'StandardHours',
'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',
'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',
'YearsSinceLastPromotion', 'YearsWithCurrManager'],
dtype='object')
outlier = ['Age', 'DailyRate', 'DistanceFromHome', 'Education', 'EmployeeCount',
'EmployeeNumber', 'EnvironmentSatisfaction', 'HourlyRate',
'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome',
'MonthlyRate', 'NumCompaniesWorked', 'PercentSalaryHike',
'PerformanceRating', 'RelationshipSatisfaction', 'StandardHours',
'StockOptionLevel', 'TotalWorkingYears', 'TrainingTimesLastYear',
'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole',
'YearsSinceLastPromotion', 'YearsWithCurrManager']
for i in outlier:
sns.boxplot(df_con[i])
plt.show()
for i in outlier:
sns.boxplot(np.cbrt(df_con[i]))
plt.show()
df_con
| Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 41 | 1102 | 1 | 2 | 1 | 1 | 2 | 94 | 3 | 2 | 4 | 5993 | 19479 | 8 | 11 | 3 | 1 | 80 | 0 | 8 | 0 | 1 | 6 | 4 | 0 | 5 |
| 1 | 49 | 279 | 8 | 1 | 1 | 2 | 3 | 61 | 2 | 2 | 2 | 5130 | 24907 | 1 | 23 | 4 | 4 | 80 | 1 | 10 | 3 | 3 | 10 | 7 | 1 | 7 |
| 2 | 37 | 1373 | 2 | 2 | 1 | 4 | 4 | 92 | 2 | 1 | 3 | 2090 | 2396 | 6 | 15 | 3 | 2 | 80 | 0 | 7 | 3 | 3 | 0 | 0 | 0 | 0 |
| 3 | 33 | 1392 | 3 | 4 | 1 | 5 | 4 | 56 | 3 | 1 | 3 | 2909 | 23159 | 1 | 11 | 3 | 3 | 80 | 0 | 8 | 3 | 3 | 8 | 7 | 3 | 0 |
| 4 | 27 | 591 | 2 | 1 | 1 | 7 | 1 | 40 | 3 | 1 | 2 | 3468 | 16632 | 9 | 12 | 3 | 4 | 80 | 1 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 36 | 884 | 23 | 2 | 1 | 2061 | 3 | 41 | 4 | 2 | 4 | 2571 | 12290 | 4 | 17 | 3 | 3 | 80 | 1 | 17 | 3 | 3 | 5 | 2 | 0 | 3 |
| 1466 | 39 | 613 | 6 | 1 | 1 | 2062 | 4 | 42 | 2 | 3 | 1 | 9991 | 21457 | 4 | 15 | 3 | 1 | 80 | 1 | 9 | 5 | 3 | 7 | 7 | 1 | 7 |
| 1467 | 27 | 155 | 4 | 3 | 1 | 2064 | 2 | 87 | 4 | 2 | 2 | 6142 | 5174 | 1 | 20 | 4 | 2 | 80 | 1 | 6 | 0 | 3 | 6 | 2 | 0 | 3 |
| 1468 | 49 | 1023 | 2 | 3 | 1 | 2065 | 4 | 63 | 2 | 2 | 2 | 5390 | 13243 | 2 | 14 | 3 | 4 | 80 | 0 | 17 | 3 | 2 | 9 | 6 | 0 | 8 |
| 1469 | 34 | 628 | 8 | 3 | 1 | 2068 | 2 | 82 | 4 | 2 | 3 | 4404 | 10228 | 2 | 12 | 3 | 1 | 80 | 0 | 6 | 3 | 4 | 4 | 3 | 1 | 2 |
1470 rows × 26 columns
df_con['Age'].unique
<bound method Series.unique of 0 41
1 49
2 37
3 33
4 27
..
1465 36
1466 39
1467 27
1468 49
1469 34
Name: Age, Length: 1470, dtype: int64>
df_con['Age'].value_counts()
35 78 34 77 36 69 31 69 29 68 32 61 30 60 38 58 33 58 40 57 37 50 27 48 28 48 42 46 39 42 45 41 41 40 26 39 44 33 46 33 43 32 50 30 24 26 25 26 47 24 49 24 55 22 53 19 48 19 51 19 52 18 54 18 22 16 56 14 23 14 58 14 21 13 20 11 59 10 19 9 18 8 60 5 57 4 Name: Age, dtype: int64
Q = []
for i in df_con.Age:
if(i<=18):
Q.append("Child")
elif(i>18 and i<=40):
Q.append("Adult")
else:
Q.append("Old")
df_cat['Age Group']=Q
df_cat
| BusinessTravel | Department | EducationField | Gender | JobRole | MaritalStatus | Over18 | OverTime | Age Group | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Travel_Rarely | Sales | Life Sciences | Female | Sales Executive | Single | Y | Yes | Old |
| 1 | Travel_Frequently | Research & Development | Life Sciences | Male | Research Scientist | Married | Y | No | Old |
| 2 | Travel_Rarely | Research & Development | Other | Male | Laboratory Technician | Single | Y | Yes | Adult |
| 3 | Travel_Frequently | Research & Development | Life Sciences | Female | Research Scientist | Married | Y | Yes | Adult |
| 4 | Travel_Rarely | Research & Development | Medical | Male | Laboratory Technician | Married | Y | No | Adult |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | Travel_Frequently | Research & Development | Medical | Male | Laboratory Technician | Married | Y | No | Adult |
| 1466 | Travel_Rarely | Research & Development | Medical | Male | Healthcare Representative | Married | Y | No | Adult |
| 1467 | Travel_Rarely | Research & Development | Life Sciences | Male | Manufacturing Director | Married | Y | Yes | Adult |
| 1468 | Travel_Frequently | Sales | Medical | Male | Sales Executive | Married | Y | No | Old |
| 1469 | Travel_Rarely | Research & Development | Medical | Male | Laboratory Technician | Married | Y | No | Adult |
1470 rows × 9 columns
df_cat = pd.get_dummies(df_cat)
df_cat
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 |
| 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 |
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
| 1466 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
| 1467 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
| 1468 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 |
| 1469 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
1470 rows × 32 columns
ss = StandardScaler()
X1 = ss.fit_transform(df_con)
X1
array([[ 0.4463504 , 0.74252653, -1.01090934, ..., -0.0632959 ,
-0.67914568, 0.24583399],
[ 1.32236521, -1.2977746 , -0.14714972, ..., 0.76499762,
-0.36871529, 0.80654148],
[ 0.008343 , 1.41436324, -0.88751511, ..., -1.16768726,
-0.67914568, -1.15593471],
...,
[-1.08667552, -1.60518328, -0.64072665, ..., -0.61549158,
-0.67914568, -0.31487349],
[ 1.32236521, 0.54667746, -0.88751511, ..., 0.48889978,
-0.67914568, 1.08689522],
[-0.32016256, -0.43256792, -0.14714972, ..., -0.33939374,
-0.36871529, -0.59522723]])
df1 = pd.DataFrame(X1,columns=df_con.columns)
df1
| Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | 1.153254 | -0.108350 | 0.726020 | 2.125136 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.421642 | -2.171982 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | 0.245834 |
| 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.660853 | -0.291719 | 1.488876 | -0.678049 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | -0.164511 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 0.806541 |
| 2 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | 0.246200 | -0.937654 | -1.674841 | 1.324226 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.550208 | 0.155707 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 |
| 3 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | 0.246200 | -0.763634 | 1.243211 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 0.252146 | -1.155935 |
| 4 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.660853 | -0.644858 | 0.325900 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.678774 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | 1.153254 | -0.835451 | -0.284329 | 0.523316 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.314873 |
| 1466 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | -1.567907 | 0.741140 | 1.004010 | 0.523316 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.293077 | 1.707500 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | 0.806541 |
| 1467 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.660853 | -0.076690 | -1.284418 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -0.678774 | -2.171982 | 0.338096 | -0.164613 | -0.615492 | -0.679146 | -0.314873 |
| 1468 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.660853 | -0.236474 | -0.150393 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.735447 | 0.155707 | -1.077862 | 0.325228 | 0.488900 | -0.679146 | 1.086895 |
| 1469 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | 0.246200 | -0.445978 | -0.574124 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 1.754054 | -0.491174 | -0.339394 | -0.368715 | -0.595227 |
1470 rows × 26 columns
df_y = df[['Attrition']]
final_df = pd.concat([df_cat,df1,df_y],axis=1)
final_df
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | Attrition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | 1.153254 | -0.108350 | 0.726020 | 2.125136 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.421642 | -2.171982 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | 0.245834 | 1 |
| 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.660853 | -0.291719 | 1.488876 | -0.678049 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | -0.164511 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 0.806541 | 0 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | 0.246200 | -0.937654 | -1.674841 | 1.324226 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.550208 | 0.155707 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 | 1 |
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | 0.246200 | -0.763634 | 1.243211 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 0.252146 | -1.155935 | 0 |
| 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.660853 | -0.644858 | 0.325900 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.678774 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | 1.153254 | -0.835451 | -0.284329 | 0.523316 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.314873 | 0 |
| 1466 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | -1.567907 | 0.741140 | 1.004010 | 0.523316 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.293077 | 1.707500 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | 0.806541 | 0 |
| 1467 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.660853 | -0.076690 | -1.284418 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -0.678774 | -2.171982 | 0.338096 | -0.164613 | -0.615492 | -0.679146 | -0.314873 | 0 |
| 1468 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.660853 | -0.236474 | -0.150393 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.735447 | 0.155707 | -1.077862 | 0.325228 | 0.488900 | -0.679146 | 1.086895 | 0 |
| 1469 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | 0.246200 | -0.445978 | -0.574124 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 1.754054 | -0.491174 | -0.339394 | -0.368715 | -0.595227 | 0 |
1470 rows × 59 columns
final_df.skew()
BusinessTravel_Non-Travel 2.632066 BusinessTravel_Travel_Frequently 1.595067 BusinessTravel_Travel_Rarely -0.923992 Department_Human Resources 4.518824 Department_Research & Development -0.646936 Department_Sales 0.856158 EducationField_Human Resources 7.181112 EducationField_Life Sciences 0.356919 EducationField_Marketing 2.525783 EducationField_Medical 0.794118 EducationField_Other 3.875119 EducationField_Technical Degree 2.872604 Gender_Female 0.408665 Gender_Male -0.408665 JobRole_Healthcare Representative 2.887251 JobRole_Human Resources 5.035637 JobRole_Laboratory Technician 1.701604 JobRole_Manager 3.392611 JobRole_Manufacturing Director 2.694844 JobRole_Research Director 3.932443 JobRole_Research Scientist 1.512214 JobRole_Sales Executive 1.340834 JobRole_Sales Representative 3.847192 MaritalStatus_Divorced 1.336093 MaritalStatus_Married 0.169484 MaritalStatus_Single 0.773874 Over18_Y 0.000000 OverTime_No -0.964489 OverTime_Yes 0.964489 Age Group_Adult -0.763830 Age Group_Child 13.458270 Age Group_Old 0.790729 Age 0.413286 DailyRate -0.003519 DistanceFromHome 0.958118 Education -0.289681 EmployeeCount 0.000000 EmployeeNumber 0.016574 EnvironmentSatisfaction -0.321654 HourlyRate -0.032311 JobInvolvement -0.498419 JobLevel 1.025401 JobSatisfaction -0.329672 MonthlyIncome 1.369817 MonthlyRate 0.018578 NumCompaniesWorked 1.026471 PercentSalaryHike 0.821128 PerformanceRating 1.921883 RelationshipSatisfaction -0.302828 StandardHours 0.000000 StockOptionLevel 0.968980 TotalWorkingYears 1.117172 TrainingTimesLastYear 0.553124 WorkLifeBalance -0.552480 YearsAtCompany 1.764529 YearsInCurrentRole 0.917363 YearsSinceLastPromotion 1.984290 YearsWithCurrManager 0.833451 Attrition 1.844366 dtype: float64
final_df.corr()
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | Attrition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BusinessTravel_Non-Travel | 1.000000 | -0.162435 | -0.526850 | -0.004755 | -0.005013 | 0.007283 | 0.020835 | 0.005311 | -0.030567 | 0.012828 | -0.013389 | 0.004171 | -0.050461 | 0.050461 | 0.012878 | -0.015890 | 0.009270 | 0.014078 | -0.013536 | -0.021431 | -0.010116 | 0.031022 | -0.033780 | 0.057455 | -0.043635 | -0.004622 | NaN | 0.037163 | -0.037163 | -0.008345 | 0.097251 | -0.007002 | -0.011215 | 0.012096 | 0.023605 | 0.004524 | NaN | 0.022272 | 0.003568 | -0.016994 | -0.045779 | -0.007295 | 0.019802 | -0.017261 | 0.015279 | 0.002718 | 0.036591 | 0.018310 | 0.021132 | NaN | 0.028807 | -0.029742 | -0.020746 | 0.005780 | 0.007623 | 0.011549 | 0.020815 | 0.016716 | -0.074457 |
| BusinessTravel_Travel_Frequently | -0.162435 | 1.000000 | -0.753092 | -0.007485 | 0.003340 | -0.000160 | 0.011818 | 0.031128 | -0.016586 | -0.005367 | -0.011004 | -0.023569 | 0.022015 | -0.022015 | 0.008029 | 0.001896 | 0.010023 | -0.042583 | 0.009783 | -0.023579 | -0.004461 | -0.010175 | 0.055469 | 0.005779 | -0.030785 | 0.027734 | NaN | -0.029392 | 0.029392 | 0.022826 | 0.011646 | -0.024772 | -0.024743 | -0.011776 | 0.005081 | -0.008292 | NaN | -0.007980 | -0.012624 | -0.018819 | 0.004424 | -0.021557 | 0.027117 | -0.031658 | 0.000344 | -0.039718 | -0.006675 | 0.016463 | 0.028500 | NaN | -0.016142 | -0.012177 | 0.006193 | 0.010199 | 0.012991 | 0.001680 | 0.023216 | 0.012628 | 0.115143 |
| BusinessTravel_Travel_Rarely | -0.526850 | -0.753092 | 1.000000 | 0.009618 | 0.000465 | -0.004718 | -0.024073 | -0.030355 | 0.034668 | -0.003930 | 0.018406 | 0.017521 | 0.014682 | -0.014682 | -0.015503 | 0.008962 | -0.014815 | 0.027294 | 0.000598 | 0.034600 | 0.010588 | -0.011920 | -0.025257 | -0.043287 | 0.055613 | -0.020808 | NaN | 0.000539 | -0.000539 | -0.014098 | -0.074876 | 0.026008 | 0.028791 | 0.002078 | -0.020116 | 0.004126 | NaN | -0.007976 | 0.008496 | 0.027541 | 0.026714 | 0.023433 | -0.036562 | 0.038779 | -0.010484 | 0.032401 | -0.018649 | -0.026390 | -0.038640 | NaN | -0.005303 | 0.030320 | 0.008498 | -0.012640 | -0.016274 | -0.009147 | -0.033877 | -0.022023 | -0.049538 |
| Department_Human Resources | -0.004755 | -0.007485 | 0.009618 | 1.000000 | -0.290754 | -0.139650 | 0.646436 | -0.068040 | -0.073692 | -0.049761 | -0.007527 | -0.019469 | -0.035652 | 0.035652 | -0.066186 | 0.904983 | -0.097859 | 0.087615 | -0.070000 | -0.050765 | -0.105352 | -0.112959 | -0.051764 | 0.016037 | 0.034767 | -0.051443 | NaN | 0.006178 | -0.006178 | -0.026808 | -0.015653 | 0.029406 | 0.020523 | -0.026726 | -0.012901 | 0.011435 | NaN | 0.063431 | -0.007597 | -0.016551 | 0.004789 | -0.006157 | -0.024068 | 0.006815 | -0.024390 | 0.020618 | -0.025888 | -0.006385 | 0.034583 | NaN | -0.004000 | 0.007508 | -0.040022 | 0.047763 | 0.007944 | -0.040287 | -0.026931 | -0.027079 | 0.016832 |
| Department_Research & Development | -0.005013 | 0.003340 | 0.000465 | -0.290754 | 1.000000 | -0.906818 | -0.187954 | 0.127321 | -0.478520 | 0.183548 | 0.064751 | 0.038541 | -0.015760 | 0.015760 | 0.227637 | -0.263128 | 0.336570 | -0.071356 | 0.240754 | 0.174596 | 0.362340 | -0.733497 | -0.336127 | 0.035158 | -0.019997 | -0.009990 | NaN | 0.003036 | -0.003036 | -0.011570 | -0.004469 | 0.012330 | 0.017883 | 0.014871 | -0.008117 | -0.018604 | NaN | -0.041923 | 0.027976 | 0.018686 | 0.023187 | -0.107830 | -0.002798 | -0.064720 | -0.005453 | 0.022237 | 0.030735 | 0.032720 | -0.004587 | NaN | 0.016927 | 0.011087 | -0.006819 | -0.069922 | -0.032181 | -0.028151 | -0.021497 | -0.014963 | -0.085293 |
| Department_Sales | 0.007283 | -0.000160 | -0.004718 | -0.139650 | -0.906818 | 1.000000 | -0.090275 | -0.101791 | 0.527691 | -0.168034 | -0.063695 | -0.031309 | 0.032017 | -0.032017 | -0.206425 | -0.126381 | -0.305208 | 0.035248 | -0.218320 | -0.158327 | -0.328576 | 0.808869 | 0.370667 | -0.043451 | 0.005378 | 0.033002 | NaN | -0.005864 | 0.005864 | 0.023785 | 0.011521 | -0.025715 | -0.027549 | -0.003616 | 0.014085 | 0.014215 | NaN | 0.015441 | -0.025606 | -0.012047 | -0.026107 | 0.114307 | 0.013499 | 0.063978 | 0.016388 | -0.032097 | -0.020403 | -0.031050 | -0.010489 | NaN | -0.015755 | -0.014781 | 0.024688 | 0.051320 | 0.029805 | 0.046883 | 0.034112 | 0.027415 | 0.080855 |
| EducationField_Human Resources | 0.020835 | 0.011818 | -0.024073 | 0.646436 | -0.187954 | -0.090275 | 1.000000 | -0.114559 | -0.047637 | -0.092899 | -0.033248 | -0.042964 | -0.028956 | 0.028956 | -0.042785 | 0.549751 | -0.063260 | 0.082271 | -0.045251 | -0.032816 | -0.068103 | -0.073020 | -0.033462 | 0.012107 | 0.057339 | -0.072051 | NaN | -0.004040 | 0.004040 | 0.007459 | -0.010119 | -0.005892 | 0.001696 | -0.043144 | -0.002624 | 0.026479 | NaN | 0.035345 | -0.006898 | -0.033670 | 0.002079 | 0.010409 | -0.021467 | 0.021456 | 0.009567 | 0.031007 | -0.016142 | -0.016167 | 0.041105 | NaN | 0.021206 | 0.005505 | -0.037664 | -0.003967 | -0.005146 | -0.025443 | -0.023700 | -0.028868 | 0.036466 |
| EducationField_Life Sciences | 0.005311 | 0.031128 | -0.030355 | -0.068040 | 0.127321 | -0.101791 | -0.114559 | 1.000000 | -0.291660 | -0.568774 | -0.203560 | -0.263050 | -0.006770 | 0.006770 | 0.029084 | -0.063119 | 0.044359 | -0.011143 | 0.052023 | 0.018401 | 0.043729 | -0.091122 | -0.043208 | -0.002672 | -0.017866 | 0.021469 | NaN | 0.013787 | -0.013787 | -0.002982 | -0.005597 | 0.003881 | 0.016824 | 0.004028 | -0.024499 | 0.013184 | NaN | -0.000609 | -0.024526 | 0.038759 | 0.003228 | -0.008431 | 0.052004 | -0.007054 | 0.025545 | -0.006131 | 0.010209 | 0.010853 | -0.019973 | NaN | -0.017993 | -0.003630 | -0.039018 | -0.039728 | -0.002019 | 0.018343 | -0.002480 | 0.003636 | -0.032703 |
| EducationField_Marketing | -0.030567 | -0.016586 | 0.034668 | -0.073692 | -0.478520 | 0.527691 | -0.047637 | -0.291660 | 1.000000 | -0.236514 | -0.084647 | -0.109385 | 0.024143 | -0.024143 | -0.108929 | -0.066690 | -0.161055 | 0.025577 | -0.115206 | -0.083548 | -0.173387 | 0.457308 | 0.133065 | -0.007212 | 0.018491 | -0.013323 | NaN | -0.014607 | 0.014607 | -0.041441 | 0.004010 | 0.040995 | 0.038162 | -0.064449 | 0.039294 | 0.072405 | NaN | -0.014487 | 0.000479 | 0.004452 | -0.018657 | 0.092698 | -0.023528 | 0.062576 | -0.011559 | -0.018611 | -0.027726 | -0.020918 | -0.006580 | NaN | 0.022560 | 0.025779 | -0.029046 | 0.018500 | 0.031365 | 0.025126 | 0.006219 | 0.033418 | 0.055781 |
| EducationField_Medical | 0.012828 | -0.005367 | -0.003930 | -0.049761 | 0.183548 | -0.168034 | -0.092899 | -0.568774 | -0.236514 | 1.000000 | -0.165072 | -0.213314 | 0.013146 | -0.013146 | 0.034165 | -0.042895 | 0.066262 | -0.001128 | 0.035496 | 0.062898 | 0.039735 | -0.133532 | -0.051990 | 0.013316 | -0.007139 | -0.004249 | NaN | -0.002246 | 0.002246 | 0.004075 | 0.029342 | -0.008736 | -0.006354 | 0.034202 | 0.013486 | -0.072335 | NaN | -0.008689 | -0.021299 | -0.020418 | 0.017103 | -0.014114 | -0.022645 | 0.001025 | -0.001723 | 0.024826 | 0.029116 | 0.014868 | 0.030494 | NaN | 0.033750 | 0.024890 | 0.070542 | 0.001641 | 0.010805 | -0.026418 | 0.022665 | -0.011544 | -0.046999 |
| EducationField_Other | -0.013389 | -0.011004 | 0.018406 | -0.007527 | 0.064751 | -0.063695 | -0.033248 | -0.203560 | -0.084647 | -0.165072 | 1.000000 | -0.076343 | -0.022992 | 0.022992 | 0.017609 | 0.001594 | 0.058759 | -0.008046 | -0.010820 | -0.006044 | 0.005286 | -0.036995 | -0.033774 | 0.005411 | -0.009171 | 0.004972 | NaN | -0.024970 | 0.024970 | 0.027823 | -0.017980 | -0.025105 | -0.041466 | -0.003893 | -0.007969 | 0.038043 | NaN | 0.010432 | 0.064602 | -0.042163 | -0.011895 | -0.016724 | 0.003380 | -0.022279 | -0.035606 | -0.012870 | 0.019297 | 0.011449 | -0.020305 | NaN | -0.042100 | -0.028935 | -0.008151 | 0.031812 | -0.030331 | -0.017021 | -0.039931 | -0.011714 | -0.017898 |
| EducationField_Technical Degree | 0.004171 | -0.023569 | 0.017521 | -0.019469 | 0.038541 | -0.031309 | -0.042964 | -0.263050 | -0.109385 | -0.213314 | -0.076343 | 1.000000 | -0.003886 | 0.003886 | 0.018681 | -0.008623 | -0.026589 | -0.038946 | 0.007817 | -0.022905 | 0.076218 | -0.058843 | 0.057185 | -0.019243 | 0.002710 | 0.014265 | NaN | 0.017723 | -0.017723 | 0.017692 | -0.023234 | -0.014097 | -0.027604 | 0.030869 | -0.014802 | -0.026742 | NaN | 0.005938 | 0.027713 | 0.011283 | -0.004519 | -0.054707 | -0.019795 | -0.049695 | -0.004535 | -0.013819 | -0.042701 | -0.021729 | -0.011044 | NaN | -0.024560 | -0.041577 | 0.008289 | 0.021962 | -0.021399 | 0.009683 | 0.003853 | -0.000836 | 0.069355 |
| Gender_Female | -0.050461 | 0.022015 | 0.014682 | -0.035652 | -0.015760 | 0.032017 | -0.028956 | -0.006770 | 0.024143 | 0.013146 | -0.022992 | -0.003886 | 1.000000 | -1.000000 | -0.006823 | -0.036082 | -0.067793 | 0.033880 | 0.065197 | 0.006121 | -0.009745 | 0.005348 | 0.028877 | -0.046076 | 0.007804 | 0.032752 | NaN | -0.041924 | 0.041924 | -0.023185 | 0.015100 | 0.020902 | 0.036311 | 0.011716 | 0.001851 | 0.016547 | NaN | -0.022556 | -0.000508 | 0.000478 | -0.017960 | 0.039403 | -0.033252 | 0.031858 | 0.041482 | 0.039147 | -0.002733 | 0.013859 | -0.022868 | NaN | -0.012716 | 0.046881 | 0.038787 | 0.002753 | 0.029747 | 0.041483 | 0.026985 | 0.030599 | -0.029453 |
| Gender_Male | 0.050461 | -0.022015 | -0.014682 | 0.035652 | 0.015760 | -0.032017 | 0.028956 | 0.006770 | -0.024143 | -0.013146 | 0.022992 | 0.003886 | -1.000000 | 1.000000 | 0.006823 | 0.036082 | 0.067793 | -0.033880 | -0.065197 | -0.006121 | 0.009745 | -0.005348 | -0.028877 | 0.046076 | -0.007804 | -0.032752 | NaN | 0.041924 | -0.041924 | 0.023185 | -0.015100 | -0.020902 | -0.036311 | -0.011716 | -0.001851 | -0.016547 | NaN | 0.022556 | 0.000508 | -0.000478 | 0.017960 | -0.039403 | 0.033252 | -0.031858 | -0.041482 | -0.039147 | 0.002733 | -0.013859 | 0.022868 | NaN | 0.012716 | -0.046881 | -0.038787 | -0.002753 | -0.029747 | -0.041483 | -0.026985 | -0.030599 | 0.029453 |
| JobRole_Healthcare Representative | 0.012878 | 0.008029 | -0.015503 | -0.066186 | 0.227637 | -0.206425 | -0.042785 | 0.029084 | -0.108929 | 0.034165 | 0.017609 | 0.018681 | -0.006823 | 0.006823 | 1.000000 | -0.059898 | -0.144652 | -0.085409 | -0.103472 | -0.075038 | -0.155727 | -0.166971 | -0.076515 | 0.027897 | 0.004913 | -0.030126 | NaN | 0.000382 | -0.000382 | -0.050336 | -0.023138 | 0.054225 | 0.098825 | 0.040141 | 0.022916 | 0.024270 | NaN | 0.025945 | 0.014090 | 0.014599 | 0.001272 | 0.115704 | 0.016367 | 0.068177 | 0.003829 | 0.026955 | 0.020591 | -0.000928 | -0.005090 | NaN | 0.014021 | 0.112159 | -0.012432 | -0.026101 | 0.069758 | 0.054695 | 0.075902 | 0.039407 | -0.078696 |
| JobRole_Human Resources | -0.015890 | 0.001896 | 0.008962 | 0.904983 | -0.263128 | -0.126381 | 0.549751 | -0.063119 | -0.066690 | -0.042895 | 0.001594 | -0.008623 | -0.036082 | 0.036082 | -0.059898 | 1.000000 | -0.088561 | -0.052290 | -0.063349 | -0.045941 | -0.095342 | -0.102226 | -0.046845 | 0.021541 | 0.030995 | -0.052320 | NaN | 0.014026 | -0.014026 | 0.021537 | -0.014166 | -0.019393 | -0.029856 | -0.021156 | -0.024089 | -0.005295 | NaN | 0.067287 | -0.022014 | -0.016189 | -0.004952 | -0.100922 | -0.029681 | -0.092250 | -0.027470 | 0.020578 | -0.021032 | -0.010154 | 0.044169 | NaN | -0.009864 | -0.076482 | -0.035902 | 0.043887 | -0.052569 | -0.057876 | -0.054603 | -0.051006 | 0.036215 |
| JobRole_Laboratory Technician | 0.009270 | 0.010023 | -0.014815 | -0.097859 | 0.336570 | -0.305208 | -0.063260 | 0.044359 | -0.161055 | 0.066262 | 0.058759 | -0.026589 | -0.067793 | 0.067793 | -0.144652 | -0.088561 | 1.000000 | -0.126280 | -0.152987 | -0.110947 | -0.230248 | -0.246873 | -0.113130 | -0.011224 | -0.009233 | 0.019873 | NaN | 0.044774 | -0.044774 | 0.100670 | 0.038601 | -0.107235 | -0.143176 | -0.006728 | 0.012369 | -0.063566 | NaN | -0.019722 | -0.001533 | 0.018028 | -0.022724 | -0.344608 | -0.015710 | -0.320906 | -0.016056 | -0.021121 | -0.020628 | 0.010796 | -0.010691 | NaN | 0.013386 | -0.215426 | 0.053998 | -0.028209 | -0.150181 | -0.131322 | -0.110099 | -0.107072 | 0.098290 |
| JobRole_Manager | 0.014078 | -0.042583 | 0.027294 | 0.087615 | -0.071356 | 0.035248 | 0.082271 | -0.011143 | 0.025577 | -0.001128 | -0.008046 | -0.038946 | 0.033880 | -0.033880 | -0.085409 | -0.052290 | -0.126280 | 1.000000 | -0.090330 | -0.065508 | -0.135949 | -0.145765 | -0.066797 | 0.001997 | 0.049982 | -0.055176 | NaN | 0.011086 | -0.011086 | -0.293287 | -0.020199 | 0.297815 | 0.294248 | -0.013224 | -0.039190 | 0.028453 | NaN | -0.035058 | 0.010730 | 0.012659 | 0.017112 | 0.552744 | -0.005620 | 0.619573 | 0.031717 | 0.042125 | -0.005394 | 0.032050 | 0.025638 | NaN | -0.015637 | 0.465837 | 0.003052 | 0.005137 | 0.330965 | 0.167499 | 0.224255 | 0.164695 | -0.083316 |
| JobRole_Manufacturing Director | -0.013536 | 0.009783 | 0.000598 | -0.070000 | 0.240754 | -0.218320 | -0.045251 | 0.052023 | -0.115206 | 0.035496 | -0.010820 | 0.007817 | 0.065197 | -0.065197 | -0.103472 | -0.063349 | -0.152987 | -0.090330 | 1.000000 | -0.079362 | -0.164700 | -0.176592 | -0.080924 | 0.020543 | 0.002819 | -0.021331 | NaN | 0.010302 | -0.010302 | 0.003206 | -0.024471 | 0.000651 | 0.049726 | -0.005302 | 0.011848 | -0.005290 | NaN | -0.014350 | 0.059178 | -0.014394 | -0.021939 | 0.114896 | -0.013747 | 0.055684 | 0.007711 | 0.009580 | 0.034682 | 0.029775 | 0.003640 | NaN | 0.007735 | 0.064077 | -0.013987 | 0.002011 | 0.031968 | 0.067877 | -0.007241 | 0.076207 | -0.082994 |
| JobRole_Research Director | -0.021431 | -0.023579 | 0.034600 | -0.050765 | 0.174596 | -0.158327 | -0.032816 | 0.018401 | -0.083548 | 0.062898 | -0.006044 | -0.022905 | 0.006121 | -0.006121 | -0.075038 | -0.045941 | -0.110947 | -0.065508 | -0.079362 | 1.000000 | -0.119442 | -0.128066 | -0.058687 | 0.037524 | 0.008271 | -0.042299 | NaN | -0.002400 | 0.002400 | -0.174980 | -0.017746 | 0.178583 | 0.185891 | -0.000021 | -0.022351 | 0.049694 | NaN | -0.013983 | -0.048689 | -0.025128 | 0.015200 | 0.414319 | -0.006217 | 0.485818 | 0.025875 | 0.097925 | -0.017017 | -0.035744 | -0.005492 | NaN | 0.015807 | 0.312148 | -0.004527 | 0.034403 | 0.153918 | 0.136332 | 0.074455 | 0.131279 | -0.088870 |
| JobRole_Research Scientist | -0.010116 | -0.004461 | 0.010588 | -0.105352 | 0.362340 | -0.328576 | -0.068103 | 0.043729 | -0.173387 | 0.039735 | 0.005286 | 0.076218 | -0.009745 | 0.009745 | -0.155727 | -0.095342 | -0.230248 | -0.135949 | -0.164700 | -0.119442 | 1.000000 | -0.265775 | -0.121792 | -0.012115 | -0.039987 | 0.053522 | NaN | -0.054378 | 0.054378 | 0.109337 | 0.009523 | -0.111340 | -0.146518 | -0.002624 | -0.010986 | 0.000709 | NaN | -0.017686 | 0.001940 | 0.020034 | 0.047604 | -0.387788 | 0.020503 | -0.345180 | -0.027008 | -0.043981 | 0.032537 | 0.019416 | -0.003116 | NaN | -0.011635 | -0.228119 | -0.052126 | -0.058613 | -0.154062 | -0.131314 | -0.105237 | -0.127608 | -0.000360 |
| JobRole_Sales Executive | 0.031022 | -0.010175 | -0.011920 | -0.112959 | -0.733497 | 0.808869 | -0.073020 | -0.091122 | 0.457308 | -0.133532 | -0.036995 | -0.058843 | 0.005348 | -0.005348 | -0.166971 | -0.102226 | -0.246873 | -0.145765 | -0.176592 | -0.128066 | -0.265775 | 1.000000 | -0.130586 | -0.013853 | 0.005751 | 0.006210 | NaN | -0.006341 | 0.006341 | 0.048711 | -0.039488 | -0.042685 | -0.002001 | -0.000513 | 0.030761 | 0.053398 | NaN | 0.023263 | -0.024421 | -0.011886 | -0.011413 | 0.127490 | 0.012604 | 0.047792 | 0.011854 | 0.005913 | -0.046683 | -0.041401 | -0.004836 | NaN | 0.015756 | -0.012241 | 0.013241 | 0.032092 | 0.042602 | 0.092349 | 0.049202 | 0.083028 | 0.019774 |
| JobRole_Sales Representative | -0.033780 | 0.055469 | -0.025257 | -0.051764 | -0.336127 | 0.370667 | -0.033462 | -0.043208 | 0.133065 | -0.051990 | -0.033774 | 0.057185 | 0.028877 | -0.028877 | -0.076515 | -0.046845 | -0.113130 | -0.066797 | -0.080924 | -0.058687 | -0.121792 | -0.130586 | 1.000000 | -0.052890 | -0.023659 | 0.072439 | NaN | -0.003347 | 0.003347 | 0.092786 | 0.102087 | -0.109357 | -0.175785 | 0.005375 | -0.015994 | -0.091465 | NaN | 0.006255 | 0.002949 | -0.018703 | -0.027282 | -0.216559 | 0.001413 | -0.201514 | -0.001200 | -0.104494 | 0.031102 | -0.006214 | -0.024859 | NaN | -0.048067 | -0.207726 | 0.040377 | 0.045148 | -0.163464 | -0.149751 | -0.085622 | -0.168743 | 0.157234 |
| MaritalStatus_Divorced | 0.057455 | 0.005779 | -0.043287 | 0.016037 | 0.035158 | -0.043451 | 0.012107 | -0.002672 | -0.007212 | 0.013316 | 0.005411 | -0.019243 | -0.046076 | 0.046076 | 0.027897 | 0.021541 | -0.011224 | 0.001997 | 0.020543 | 0.037524 | -0.012115 | -0.013853 | -0.052890 | 1.000000 | -0.491506 | -0.366691 | NaN | -0.023462 | 0.023462 | -0.016742 | -0.039566 | 0.023078 | 0.033120 | 0.037080 | -0.005440 | -0.002439 | NaN | -0.025149 | 0.016439 | -0.006150 | 0.016815 | 0.037087 | -0.015197 | 0.032203 | -0.000227 | 0.040824 | -0.023478 | -0.010310 | 0.006199 | NaN | 0.446285 | 0.036291 | 0.008405 | -0.009080 | 0.025728 | 0.018532 | -0.005279 | 0.014095 | -0.087716 |
| MaritalStatus_Married | -0.043635 | -0.030785 | 0.055613 | 0.034767 | -0.019997 | 0.005378 | 0.057339 | -0.017866 | 0.018491 | -0.007139 | -0.009171 | 0.002710 | 0.007804 | -0.007804 | 0.004913 | 0.030995 | -0.009233 | 0.049982 | 0.002819 | 0.008271 | -0.039987 | 0.005751 | -0.023659 | -0.491506 | 1.000000 | -0.629981 | NaN | 0.013502 | -0.013502 | -0.036388 | -0.067975 | 0.047307 | 0.083919 | 0.040035 | 0.030232 | -0.001865 | NaN | 0.053933 | -0.022180 | 0.036432 | 0.028324 | 0.050547 | -0.010315 | 0.056767 | -0.034689 | -0.016142 | 0.020895 | 0.009585 | -0.043382 | NaN | 0.225574 | 0.053512 | -0.029602 | -0.006388 | 0.044925 | 0.065488 | 0.054102 | 0.032972 | -0.090984 |
| MaritalStatus_Single | -0.004622 | 0.027734 | -0.020808 | -0.051443 | -0.009990 | 0.033002 | -0.072051 | 0.021469 | -0.013323 | -0.004249 | 0.004972 | 0.014265 | 0.032752 | -0.032752 | -0.030126 | -0.052320 | 0.019873 | -0.055176 | -0.021331 | -0.042299 | 0.053522 | 0.006210 | 0.072439 | -0.366691 | -0.629981 | 1.000000 | NaN | 0.006498 | -0.006498 | 0.053803 | 0.107900 | -0.071118 | -0.119185 | -0.075835 | -0.027445 | 0.004168 | NaN | -0.035189 | 0.009035 | -0.033436 | -0.045253 | -0.087072 | 0.024571 | -0.089361 | 0.037260 | -0.019161 | -0.001386 | -0.001045 | 0.040817 | NaN | -0.638957 | -0.089529 | 0.024129 | 0.014921 | -0.070935 | -0.086486 | -0.053090 | -0.047793 | 0.175419 |
| Over18_Y | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| OverTime_No | 0.037163 | -0.029392 | 0.000539 | 0.006178 | 0.003036 | -0.005864 | -0.004040 | 0.013787 | -0.014607 | -0.002246 | -0.024970 | 0.017723 | -0.041924 | 0.041924 | 0.000382 | 0.014026 | 0.044774 | 0.011086 | 0.010302 | -0.002400 | -0.054378 | -0.006341 | -0.003347 | -0.023462 | 0.013502 | 0.006498 | NaN | 1.000000 | -1.000000 | 0.039259 | 0.005418 | -0.040295 | -0.028062 | -0.009135 | -0.025514 | 0.020322 | NaN | 0.024037 | -0.070132 | 0.007782 | 0.003507 | -0.000544 | -0.024539 | -0.006089 | -0.021431 | 0.020786 | 0.005433 | -0.004369 | -0.048493 | NaN | 0.000449 | -0.012754 | 0.079113 | 0.027092 | 0.011687 | 0.029758 | 0.012239 | 0.041586 | -0.246118 |
| OverTime_Yes | -0.037163 | 0.029392 | -0.000539 | -0.006178 | -0.003036 | 0.005864 | 0.004040 | -0.013787 | 0.014607 | 0.002246 | 0.024970 | -0.017723 | 0.041924 | -0.041924 | -0.000382 | -0.014026 | -0.044774 | -0.011086 | -0.010302 | 0.002400 | 0.054378 | 0.006341 | 0.003347 | 0.023462 | -0.013502 | -0.006498 | NaN | -1.000000 | 1.000000 | -0.039259 | -0.005418 | 0.040295 | 0.028062 | 0.009135 | 0.025514 | -0.020322 | NaN | -0.024037 | 0.070132 | -0.007782 | -0.003507 | 0.000544 | 0.024539 | 0.006089 | 0.021431 | -0.020786 | -0.005433 | 0.004369 | 0.048493 | NaN | -0.000449 | 0.012754 | -0.079113 | -0.027092 | -0.011687 | -0.029758 | -0.012239 | -0.041586 | 0.246118 |
| Age Group_Adult | -0.008345 | 0.022826 | -0.014098 | -0.026808 | -0.011570 | 0.023785 | 0.007459 | -0.002982 | -0.041441 | 0.004075 | 0.027823 | 0.017692 | -0.023185 | 0.023185 | -0.050336 | 0.021537 | 0.100670 | -0.293287 | 0.003206 | -0.174980 | 0.109337 | 0.048711 | 0.092786 | -0.016742 | -0.036388 | 0.053803 | NaN | 0.039259 | -0.039259 | 1.000000 | -0.107396 | -0.987553 | -0.791869 | -0.004701 | -0.005739 | -0.102673 | NaN | 0.017838 | -0.002137 | -0.019811 | -0.036322 | -0.423418 | 0.007416 | -0.423949 | -0.018712 | -0.226912 | 0.001236 | 0.002906 | -0.052690 | NaN | 0.011119 | -0.543259 | 0.002348 | 0.000122 | -0.226865 | -0.125668 | -0.147455 | -0.113396 | 0.080224 |
| Age Group_Child | 0.097251 | 0.011646 | -0.074876 | -0.015653 | -0.004469 | 0.011521 | -0.010119 | -0.005597 | 0.004010 | 0.029342 | -0.017980 | -0.023234 | 0.015100 | -0.015100 | -0.023138 | -0.014166 | 0.038601 | -0.020199 | -0.024471 | -0.017746 | 0.009523 | -0.039488 | 0.102087 | -0.039566 | -0.067975 | 0.107900 | NaN | 0.005418 | -0.005418 | -0.107396 | 1.000000 | -0.050317 | -0.153286 | -0.010061 | -0.028000 | -0.029835 | NaN | 0.003535 | 0.001911 | 0.008131 | 0.015086 | -0.071124 | 0.034986 | -0.078345 | 0.001694 | -0.050157 | -0.032036 | -0.031529 | 0.028249 | NaN | -0.068944 | -0.107273 | -0.017179 | 0.025010 | -0.084647 | -0.086377 | -0.050238 | -0.085508 | 0.068147 |
| Age Group_Old | -0.007002 | -0.024772 | 0.026008 | 0.029406 | 0.012330 | -0.025715 | -0.005892 | 0.003881 | 0.040995 | -0.008736 | -0.025105 | -0.014097 | 0.020902 | -0.020902 | 0.054225 | -0.019393 | -0.107235 | 0.297815 | 0.000651 | 0.178583 | -0.111340 | -0.042685 | -0.109357 | 0.023078 | 0.047307 | -0.071118 | NaN | -0.040295 | 0.040295 | -0.987553 | -0.050317 | 1.000000 | 0.819717 | 0.006314 | 0.010195 | 0.107859 | NaN | -0.018479 | 0.001844 | 0.018615 | 0.034100 | 0.436593 | -0.012984 | 0.438270 | 0.018529 | 0.235877 | 0.003827 | 0.002069 | 0.048460 | NaN | -0.000263 | 0.562698 | 0.000359 | -0.004079 | 0.241287 | 0.139904 | 0.156073 | 0.127439 | -0.091370 |
| Age | -0.011215 | -0.024743 | 0.028791 | 0.020523 | 0.017883 | -0.027549 | 0.001696 | 0.016824 | 0.038162 | -0.006354 | -0.041466 | -0.027604 | 0.036311 | -0.036311 | 0.098825 | -0.029856 | -0.143176 | 0.294248 | 0.049726 | 0.185891 | -0.146518 | -0.002001 | -0.175785 | 0.033120 | 0.083919 | -0.119185 | NaN | -0.028062 | 0.028062 | -0.791869 | -0.153286 | 0.819717 | 1.000000 | 0.010661 | -0.001686 | 0.208034 | NaN | -0.010145 | 0.010146 | 0.024287 | 0.029820 | 0.509604 | -0.004892 | 0.497855 | 0.028051 | 0.299635 | 0.003634 | 0.001904 | 0.053535 | NaN | 0.037510 | 0.680381 | -0.019621 | -0.021490 | 0.311309 | 0.212901 | 0.216513 | 0.202089 | -0.159205 |
| DailyRate | 0.012096 | -0.011776 | 0.002078 | -0.026726 | 0.014871 | -0.003616 | -0.043144 | 0.004028 | -0.064449 | 0.034202 | -0.003893 | 0.030869 | 0.011716 | -0.011716 | 0.040141 | -0.021156 | -0.006728 | -0.013224 | -0.005302 | -0.000021 | -0.002624 | -0.000513 | 0.005375 | 0.037080 | 0.040035 | -0.075835 | NaN | -0.009135 | 0.009135 | -0.004701 | -0.010061 | 0.006314 | 0.010661 | 1.000000 | -0.004985 | -0.016806 | NaN | -0.050990 | 0.018355 | 0.023381 | 0.046135 | 0.002966 | 0.030571 | 0.007707 | -0.032182 | 0.038153 | 0.022704 | 0.000473 | 0.007846 | NaN | 0.042143 | 0.014515 | 0.002453 | -0.037848 | -0.034055 | 0.009932 | -0.033229 | -0.026363 | -0.056652 |
| DistanceFromHome | 0.023605 | 0.005081 | -0.020116 | -0.012901 | -0.008117 | 0.014085 | -0.002624 | -0.024499 | 0.039294 | 0.013486 | -0.007969 | -0.014802 | 0.001851 | -0.001851 | 0.022916 | -0.024089 | 0.012369 | -0.039190 | 0.011848 | -0.022351 | -0.010986 | 0.030761 | -0.015994 | -0.005440 | 0.030232 | -0.027445 | NaN | -0.025514 | 0.025514 | -0.005739 | -0.028000 | 0.010195 | -0.001686 | -0.004985 | 1.000000 | 0.021042 | NaN | 0.032916 | -0.016075 | 0.031131 | 0.008783 | 0.005303 | -0.003669 | -0.017014 | 0.027473 | -0.029251 | 0.040235 | 0.027110 | 0.006557 | NaN | 0.044872 | 0.004628 | -0.036942 | -0.026556 | 0.009508 | 0.018845 | 0.010029 | 0.014406 | 0.077924 |
| Education | 0.004524 | -0.008292 | 0.004126 | 0.011435 | -0.018604 | 0.014215 | 0.026479 | 0.013184 | 0.072405 | -0.072335 | 0.038043 | -0.026742 | 0.016547 | -0.016547 | 0.024270 | -0.005295 | -0.063566 | 0.028453 | -0.005290 | 0.049694 | 0.000709 | 0.053398 | -0.091465 | -0.002439 | -0.001865 | 0.004168 | NaN | 0.020322 | -0.020322 | -0.102673 | -0.029835 | 0.107859 | 0.208034 | -0.016806 | 0.021042 | 1.000000 | NaN | 0.042070 | -0.027128 | 0.016775 | 0.042438 | 0.101589 | -0.011296 | 0.094961 | -0.026084 | 0.126317 | -0.011111 | -0.024539 | -0.009118 | NaN | 0.018422 | 0.148280 | -0.025100 | 0.009819 | 0.069114 | 0.060236 | 0.054254 | 0.069065 | -0.031373 |
| EmployeeCount | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| EmployeeNumber | 0.022272 | -0.007980 | -0.007976 | 0.063431 | -0.041923 | 0.015441 | 0.035345 | -0.000609 | -0.014487 | -0.008689 | 0.010432 | 0.005938 | -0.022556 | 0.022556 | 0.025945 | 0.067287 | -0.019722 | -0.035058 | -0.014350 | -0.013983 | -0.017686 | 0.023263 | 0.006255 | -0.025149 | 0.053933 | -0.035189 | NaN | 0.024037 | -0.024037 | 0.017838 | 0.003535 | -0.018479 | -0.010145 | -0.050990 | 0.032916 | 0.042070 | NaN | 1.000000 | 0.017621 | 0.035179 | -0.006888 | -0.018519 | -0.046247 | -0.014829 | 0.012648 | -0.001251 | -0.012944 | -0.020359 | -0.069861 | NaN | 0.062227 | -0.014365 | 0.023603 | 0.010309 | -0.011240 | -0.008416 | -0.009019 | -0.009197 | -0.010577 |
| EnvironmentSatisfaction | 0.003568 | -0.012624 | 0.008496 | -0.007597 | 0.027976 | -0.025606 | -0.006898 | -0.024526 | 0.000479 | -0.021299 | 0.064602 | 0.027713 | -0.000508 | 0.000508 | 0.014090 | -0.022014 | -0.001533 | 0.010730 | 0.059178 | -0.048689 | 0.001940 | -0.024421 | 0.002949 | 0.016439 | -0.022180 | 0.009035 | NaN | -0.070132 | 0.070132 | -0.002137 | 0.001911 | 0.001844 | 0.010146 | 0.018355 | -0.016075 | -0.027128 | NaN | 0.017621 | 1.000000 | -0.049857 | -0.008278 | 0.001212 | -0.006784 | -0.006259 | 0.037600 | 0.012594 | -0.031701 | -0.029548 | 0.007665 | NaN | 0.003432 | -0.002693 | -0.019359 | 0.027627 | 0.001458 | 0.018007 | 0.016194 | -0.004999 | -0.103369 |
| HourlyRate | -0.016994 | -0.018819 | 0.027541 | -0.016551 | 0.018686 | -0.012047 | -0.033670 | 0.038759 | 0.004452 | -0.020418 | -0.042163 | 0.011283 | 0.000478 | -0.000478 | 0.014599 | -0.016189 | 0.018028 | 0.012659 | -0.014394 | -0.025128 | 0.020034 | -0.011886 | -0.018703 | -0.006150 | 0.036432 | -0.033436 | NaN | 0.007782 | -0.007782 | -0.019811 | 0.008131 | 0.018615 | 0.024287 | 0.023381 | 0.031131 | 0.016775 | NaN | 0.035179 | -0.049857 | 1.000000 | 0.042861 | -0.027853 | -0.071335 | -0.015794 | -0.015297 | 0.022157 | -0.009062 | -0.002172 | 0.001330 | NaN | 0.050263 | -0.002334 | -0.008548 | -0.004607 | -0.019582 | -0.024106 | -0.026716 | -0.020123 | -0.006846 |
| JobInvolvement | -0.045779 | 0.004424 | 0.026714 | 0.004789 | 0.023187 | -0.026107 | 0.002079 | 0.003228 | -0.018657 | 0.017103 | -0.011895 | -0.004519 | -0.017960 | 0.017960 | 0.001272 | -0.004952 | -0.022724 | 0.017112 | -0.021939 | 0.015200 | 0.047604 | -0.011413 | -0.027282 | 0.016815 | 0.028324 | -0.045253 | NaN | 0.003507 | -0.003507 | -0.036322 | 0.015086 | 0.034100 | 0.029820 | 0.046135 | 0.008783 | 0.042438 | NaN | -0.006888 | -0.008278 | 0.042861 | 1.000000 | -0.012630 | -0.021476 | -0.015271 | -0.016322 | 0.015012 | -0.017205 | -0.029071 | 0.034297 | NaN | 0.021523 | -0.005533 | -0.015338 | -0.014617 | -0.021355 | 0.008717 | -0.024184 | 0.025976 | -0.130016 |
| JobLevel | -0.007295 | -0.021557 | 0.023433 | -0.006157 | -0.107830 | 0.114307 | 0.010409 | -0.008431 | 0.092698 | -0.014114 | -0.016724 | -0.054707 | 0.039403 | -0.039403 | 0.115704 | -0.100922 | -0.344608 | 0.552744 | 0.114896 | 0.414319 | -0.387788 | 0.127490 | -0.216559 | 0.037087 | 0.050547 | -0.087072 | NaN | -0.000544 | 0.000544 | -0.423418 | -0.071124 | 0.436593 | 0.509604 | 0.002966 | 0.005303 | 0.101589 | NaN | -0.018519 | 0.001212 | -0.027853 | -0.012630 | 1.000000 | -0.001944 | 0.950300 | 0.039563 | 0.142501 | -0.034730 | -0.021222 | 0.021642 | NaN | 0.013984 | 0.782208 | -0.018191 | 0.037818 | 0.534739 | 0.389447 | 0.353885 | 0.375281 | -0.169105 |
| JobSatisfaction | 0.019802 | 0.027117 | -0.036562 | -0.024068 | -0.002798 | 0.013499 | -0.021467 | 0.052004 | -0.023528 | -0.022645 | 0.003380 | -0.019795 | -0.033252 | 0.033252 | 0.016367 | -0.029681 | -0.015710 | -0.005620 | -0.013747 | -0.006217 | 0.020503 | 0.012604 | 0.001413 | -0.015197 | -0.010315 | 0.024571 | NaN | -0.024539 | 0.024539 | 0.007416 | 0.034986 | -0.012984 | -0.004892 | 0.030571 | -0.003669 | -0.011296 | NaN | -0.046247 | -0.006784 | -0.071335 | -0.021476 | -0.001944 | 1.000000 | -0.007157 | 0.000644 | -0.055699 | 0.020002 | 0.002297 | -0.012454 | NaN | 0.010690 | -0.020185 | -0.005779 | -0.019459 | -0.003803 | -0.002305 | -0.018214 | -0.027656 | -0.103481 |
| MonthlyIncome | -0.017261 | -0.031658 | 0.038779 | 0.006815 | -0.064720 | 0.063978 | 0.021456 | -0.007054 | 0.062576 | 0.001025 | -0.022279 | -0.049695 | 0.031858 | -0.031858 | 0.068177 | -0.092250 | -0.320906 | 0.619573 | 0.055684 | 0.485818 | -0.345180 | 0.047792 | -0.201514 | 0.032203 | 0.056767 | -0.089361 | NaN | -0.006089 | 0.006089 | -0.423949 | -0.078345 | 0.438270 | 0.497855 | 0.007707 | -0.017014 | 0.094961 | NaN | -0.014829 | -0.006259 | -0.015794 | -0.015271 | 0.950300 | -0.007157 | 1.000000 | 0.034814 | 0.149515 | -0.027269 | -0.017120 | 0.025873 | NaN | 0.005408 | 0.772893 | -0.021736 | 0.030683 | 0.514285 | 0.363818 | 0.344978 | 0.344079 | -0.159840 |
| MonthlyRate | 0.015279 | 0.000344 | -0.010484 | -0.024390 | -0.005453 | 0.016388 | 0.009567 | 0.025545 | -0.011559 | -0.001723 | -0.035606 | -0.004535 | 0.041482 | -0.041482 | 0.003829 | -0.027470 | -0.016056 | 0.031717 | 0.007711 | 0.025875 | -0.027008 | 0.011854 | -0.001200 | -0.000227 | -0.034689 | 0.037260 | NaN | -0.021431 | 0.021431 | -0.018712 | 0.001694 | 0.018529 | 0.028051 | -0.032182 | 0.027473 | -0.026084 | NaN | 0.012648 | 0.037600 | -0.015297 | -0.016322 | 0.039563 | 0.000644 | 0.034814 | 1.000000 | 0.017521 | -0.006429 | -0.009811 | -0.004085 | NaN | -0.034323 | 0.026442 | 0.001467 | 0.007963 | -0.023655 | -0.012815 | 0.001567 | -0.036746 | 0.015170 |
| NumCompaniesWorked | 0.002718 | -0.039718 | 0.032401 | 0.020618 | 0.022237 | -0.032097 | 0.031007 | -0.006131 | -0.018611 | 0.024826 | -0.012870 | -0.013819 | 0.039147 | -0.039147 | 0.026955 | 0.020578 | -0.021121 | 0.042125 | 0.009580 | 0.097925 | -0.043981 | 0.005913 | -0.104494 | 0.040824 | -0.016142 | -0.019161 | NaN | 0.020786 | -0.020786 | -0.226912 | -0.050157 | 0.235877 | 0.299635 | 0.038153 | -0.029251 | 0.126317 | NaN | -0.001251 | 0.012594 | 0.022157 | 0.015012 | 0.142501 | -0.055699 | 0.149515 | 0.017521 | 1.000000 | -0.010238 | -0.014095 | 0.052733 | NaN | 0.030075 | 0.237639 | -0.066054 | -0.008366 | -0.118421 | -0.090754 | -0.036814 | -0.110319 | 0.043494 |
| PercentSalaryHike | 0.036591 | -0.006675 | -0.018649 | -0.025888 | 0.030735 | -0.020403 | -0.016142 | 0.010209 | -0.027726 | 0.029116 | 0.019297 | -0.042701 | -0.002733 | 0.002733 | 0.020591 | -0.021032 | -0.020628 | -0.005394 | 0.034682 | -0.017017 | 0.032537 | -0.046683 | 0.031102 | -0.023478 | 0.020895 | -0.001386 | NaN | 0.005433 | -0.005433 | 0.001236 | -0.032036 | 0.003827 | 0.003634 | 0.022704 | 0.040235 | -0.011111 | NaN | -0.012944 | -0.031701 | -0.009062 | -0.017205 | -0.034730 | 0.020002 | -0.027269 | -0.006429 | -0.010238 | 1.000000 | 0.773550 | -0.040490 | NaN | 0.007528 | -0.020608 | -0.005221 | -0.003280 | -0.035991 | -0.001520 | -0.022154 | -0.011985 | -0.013478 |
| PerformanceRating | 0.018310 | 0.016463 | -0.026390 | -0.006385 | 0.032720 | -0.031050 | -0.016167 | 0.010853 | -0.020918 | 0.014868 | 0.011449 | -0.021729 | 0.013859 | -0.013859 | -0.000928 | -0.010154 | 0.010796 | 0.032050 | 0.029775 | -0.035744 | 0.019416 | -0.041401 | -0.006214 | -0.010310 | 0.009585 | -0.001045 | NaN | -0.004369 | 0.004369 | 0.002906 | -0.031529 | 0.002069 | 0.001904 | 0.000473 | 0.027110 | -0.024539 | NaN | -0.020359 | -0.029548 | -0.002172 | -0.029071 | -0.021222 | 0.002297 | -0.017120 | -0.009811 | -0.014095 | 0.773550 | 1.000000 | -0.031351 | NaN | 0.003506 | 0.006744 | -0.015579 | 0.002572 | 0.003435 | 0.034986 | 0.017896 | 0.022827 | 0.002889 |
| RelationshipSatisfaction | 0.021132 | 0.028500 | -0.038640 | 0.034583 | -0.004587 | -0.010489 | 0.041105 | -0.019973 | -0.006580 | 0.030494 | -0.020305 | -0.011044 | -0.022868 | 0.022868 | -0.005090 | 0.044169 | -0.010691 | 0.025638 | 0.003640 | -0.005492 | -0.003116 | -0.004836 | -0.024859 | 0.006199 | -0.043382 | 0.040817 | NaN | -0.048493 | 0.048493 | -0.052690 | 0.028249 | 0.048460 | 0.053535 | 0.007846 | 0.006557 | -0.009118 | NaN | -0.069861 | 0.007665 | 0.001330 | 0.034297 | 0.021642 | -0.012454 | 0.025873 | -0.004085 | 0.052733 | -0.040490 | -0.031351 | 1.000000 | NaN | -0.045952 | 0.024054 | 0.002497 | 0.019604 | 0.019367 | -0.015123 | 0.033493 | -0.000867 | -0.045872 |
| StandardHours | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| StockOptionLevel | 0.028807 | -0.016142 | -0.005303 | -0.004000 | 0.016927 | -0.015755 | 0.021206 | -0.017993 | 0.022560 | 0.033750 | -0.042100 | -0.024560 | -0.012716 | 0.012716 | 0.014021 | -0.009864 | 0.013386 | -0.015637 | 0.007735 | 0.015807 | -0.011635 | 0.015756 | -0.048067 | 0.446285 | 0.225574 | -0.638957 | NaN | 0.000449 | -0.000449 | 0.011119 | -0.068944 | -0.000263 | 0.037510 | 0.042143 | 0.044872 | 0.018422 | NaN | 0.062227 | 0.003432 | 0.050263 | 0.021523 | 0.013984 | 0.010690 | 0.005408 | -0.034323 | 0.030075 | 0.007528 | 0.003506 | -0.045952 | NaN | 1.000000 | 0.010136 | 0.011274 | 0.004129 | 0.015058 | 0.050818 | 0.014352 | 0.024698 | -0.137145 |
| TotalWorkingYears | -0.029742 | -0.012177 | 0.030320 | 0.007508 | 0.011087 | -0.014781 | 0.005505 | -0.003630 | 0.025779 | 0.024890 | -0.028935 | -0.041577 | 0.046881 | -0.046881 | 0.112159 | -0.076482 | -0.215426 | 0.465837 | 0.064077 | 0.312148 | -0.228119 | -0.012241 | -0.207726 | 0.036291 | 0.053512 | -0.089529 | NaN | -0.012754 | 0.012754 | -0.543259 | -0.107273 | 0.562698 | 0.680381 | 0.014515 | 0.004628 | 0.148280 | NaN | -0.014365 | -0.002693 | -0.002334 | -0.005533 | 0.782208 | -0.020185 | 0.772893 | 0.026442 | 0.237639 | -0.020608 | 0.006744 | 0.024054 | NaN | 0.010136 | 1.000000 | -0.035662 | 0.001008 | 0.628133 | 0.460365 | 0.404858 | 0.459188 | -0.171063 |
| TrainingTimesLastYear | -0.020746 | 0.006193 | 0.008498 | -0.040022 | -0.006819 | 0.024688 | -0.037664 | -0.039018 | -0.029046 | 0.070542 | -0.008151 | 0.008289 | 0.038787 | -0.038787 | -0.012432 | -0.035902 | 0.053998 | 0.003052 | -0.013987 | -0.004527 | -0.052126 | 0.013241 | 0.040377 | 0.008405 | -0.029602 | 0.024129 | NaN | 0.079113 | -0.079113 | 0.002348 | -0.017179 | 0.000359 | -0.019621 | 0.002453 | -0.036942 | -0.025100 | NaN | 0.023603 | -0.019359 | -0.008548 | -0.015338 | -0.018191 | -0.005779 | -0.021736 | 0.001467 | -0.066054 | -0.005221 | -0.015579 | 0.002497 | NaN | 0.011274 | -0.035662 | 1.000000 | 0.028072 | 0.003569 | -0.005738 | -0.002067 | -0.004096 | -0.059478 |
| WorkLifeBalance | 0.005780 | 0.010199 | -0.012640 | 0.047763 | -0.069922 | 0.051320 | -0.003967 | -0.039728 | 0.018500 | 0.001641 | 0.031812 | 0.021962 | 0.002753 | -0.002753 | -0.026101 | 0.043887 | -0.028209 | 0.005137 | 0.002011 | 0.034403 | -0.058613 | 0.032092 | 0.045148 | -0.009080 | -0.006388 | 0.014921 | NaN | 0.027092 | -0.027092 | 0.000122 | 0.025010 | -0.004079 | -0.021490 | -0.037848 | -0.026556 | 0.009819 | NaN | 0.010309 | 0.027627 | -0.004607 | -0.014617 | 0.037818 | -0.019459 | 0.030683 | 0.007963 | -0.008366 | -0.003280 | 0.002572 | 0.019604 | NaN | 0.004129 | 0.001008 | 0.028072 | 1.000000 | 0.012089 | 0.049856 | 0.008941 | 0.002759 | -0.063939 |
| YearsAtCompany | 0.007623 | 0.012991 | -0.016274 | 0.007944 | -0.032181 | 0.029805 | -0.005146 | -0.002019 | 0.031365 | 0.010805 | -0.030331 | -0.021399 | 0.029747 | -0.029747 | 0.069758 | -0.052569 | -0.150181 | 0.330965 | 0.031968 | 0.153918 | -0.154062 | 0.042602 | -0.163464 | 0.025728 | 0.044925 | -0.070935 | NaN | 0.011687 | -0.011687 | -0.226865 | -0.084647 | 0.241287 | 0.311309 | -0.034055 | 0.009508 | 0.069114 | NaN | -0.011240 | 0.001458 | -0.019582 | -0.021355 | 0.534739 | -0.003803 | 0.514285 | -0.023655 | -0.118421 | -0.035991 | 0.003435 | 0.019367 | NaN | 0.015058 | 0.628133 | 0.003569 | 0.012089 | 1.000000 | 0.758754 | 0.618409 | 0.769212 | -0.134392 |
| YearsInCurrentRole | 0.011549 | 0.001680 | -0.009147 | -0.040287 | -0.028151 | 0.046883 | -0.025443 | 0.018343 | 0.025126 | -0.026418 | -0.017021 | 0.009683 | 0.041483 | -0.041483 | 0.054695 | -0.057876 | -0.131322 | 0.167499 | 0.067877 | 0.136332 | -0.131314 | 0.092349 | -0.149751 | 0.018532 | 0.065488 | -0.086486 | NaN | 0.029758 | -0.029758 | -0.125668 | -0.086377 | 0.139904 | 0.212901 | 0.009932 | 0.018845 | 0.060236 | NaN | -0.008416 | 0.018007 | -0.024106 | 0.008717 | 0.389447 | -0.002305 | 0.363818 | -0.012815 | -0.090754 | -0.001520 | 0.034986 | -0.015123 | NaN | 0.050818 | 0.460365 | -0.005738 | 0.049856 | 0.758754 | 1.000000 | 0.548056 | 0.714365 | -0.160545 |
| YearsSinceLastPromotion | 0.020815 | 0.023216 | -0.033877 | -0.026931 | -0.021497 | 0.034112 | -0.023700 | -0.002480 | 0.006219 | 0.022665 | -0.039931 | 0.003853 | 0.026985 | -0.026985 | 0.075902 | -0.054603 | -0.110099 | 0.224255 | -0.007241 | 0.074455 | -0.105237 | 0.049202 | -0.085622 | -0.005279 | 0.054102 | -0.053090 | NaN | 0.012239 | -0.012239 | -0.147455 | -0.050238 | 0.156073 | 0.216513 | -0.033229 | 0.010029 | 0.054254 | NaN | -0.009019 | 0.016194 | -0.026716 | -0.024184 | 0.353885 | -0.018214 | 0.344978 | 0.001567 | -0.036814 | -0.022154 | 0.017896 | 0.033493 | NaN | 0.014352 | 0.404858 | -0.002067 | 0.008941 | 0.618409 | 0.548056 | 1.000000 | 0.510224 | -0.033019 |
| YearsWithCurrManager | 0.016716 | 0.012628 | -0.022023 | -0.027079 | -0.014963 | 0.027415 | -0.028868 | 0.003636 | 0.033418 | -0.011544 | -0.011714 | -0.000836 | 0.030599 | -0.030599 | 0.039407 | -0.051006 | -0.107072 | 0.164695 | 0.076207 | 0.131279 | -0.127608 | 0.083028 | -0.168743 | 0.014095 | 0.032972 | -0.047793 | NaN | 0.041586 | -0.041586 | -0.113396 | -0.085508 | 0.127439 | 0.202089 | -0.026363 | 0.014406 | 0.069065 | NaN | -0.009197 | -0.004999 | -0.020123 | 0.025976 | 0.375281 | -0.027656 | 0.344079 | -0.036746 | -0.110319 | -0.011985 | 0.022827 | -0.000867 | NaN | 0.024698 | 0.459188 | -0.004096 | 0.002759 | 0.769212 | 0.714365 | 0.510224 | 1.000000 | -0.156199 |
| Attrition | -0.074457 | 0.115143 | -0.049538 | 0.016832 | -0.085293 | 0.080855 | 0.036466 | -0.032703 | 0.055781 | -0.046999 | -0.017898 | 0.069355 | -0.029453 | 0.029453 | -0.078696 | 0.036215 | 0.098290 | -0.083316 | -0.082994 | -0.088870 | -0.000360 | 0.019774 | 0.157234 | -0.087716 | -0.090984 | 0.175419 | NaN | -0.246118 | 0.246118 | 0.080224 | 0.068147 | -0.091370 | -0.159205 | -0.056652 | 0.077924 | -0.031373 | NaN | -0.010577 | -0.103369 | -0.006846 | -0.130016 | -0.169105 | -0.103481 | -0.159840 | 0.015170 | 0.043494 | -0.013478 | 0.002889 | -0.045872 | NaN | -0.137145 | -0.171063 | -0.059478 | -0.063939 | -0.134392 | -0.160545 | -0.033019 | -0.156199 | 1.000000 |
plt.figure(figsize=(100,100))
sns.heatmap(final_df.corr(),annot=True)
<AxesSubplot:>
final_df.corr()['Attrition']
BusinessTravel_Non-Travel -0.074457 BusinessTravel_Travel_Frequently 0.115143 BusinessTravel_Travel_Rarely -0.049538 Department_Human Resources 0.016832 Department_Research & Development -0.085293 Department_Sales 0.080855 EducationField_Human Resources 0.036466 EducationField_Life Sciences -0.032703 EducationField_Marketing 0.055781 EducationField_Medical -0.046999 EducationField_Other -0.017898 EducationField_Technical Degree 0.069355 Gender_Female -0.029453 Gender_Male 0.029453 JobRole_Healthcare Representative -0.078696 JobRole_Human Resources 0.036215 JobRole_Laboratory Technician 0.098290 JobRole_Manager -0.083316 JobRole_Manufacturing Director -0.082994 JobRole_Research Director -0.088870 JobRole_Research Scientist -0.000360 JobRole_Sales Executive 0.019774 JobRole_Sales Representative 0.157234 MaritalStatus_Divorced -0.087716 MaritalStatus_Married -0.090984 MaritalStatus_Single 0.175419 Over18_Y NaN OverTime_No -0.246118 OverTime_Yes 0.246118 Age Group_Adult 0.080224 Age Group_Child 0.068147 Age Group_Old -0.091370 Age -0.159205 DailyRate -0.056652 DistanceFromHome 0.077924 Education -0.031373 EmployeeCount NaN EmployeeNumber -0.010577 EnvironmentSatisfaction -0.103369 HourlyRate -0.006846 JobInvolvement -0.130016 JobLevel -0.169105 JobSatisfaction -0.103481 MonthlyIncome -0.159840 MonthlyRate 0.015170 NumCompaniesWorked 0.043494 PercentSalaryHike -0.013478 PerformanceRating 0.002889 RelationshipSatisfaction -0.045872 StandardHours NaN StockOptionLevel -0.137145 TotalWorkingYears -0.171063 TrainingTimesLastYear -0.059478 WorkLifeBalance -0.063939 YearsAtCompany -0.134392 YearsInCurrentRole -0.160545 YearsSinceLastPromotion -0.033019 YearsWithCurrManager -0.156199 Attrition 1.000000 Name: Attrition, dtype: float64
plt.figure(figsize=(20,40))
x=1
for i in df.columns:
if(df[i].dtypes=="object"):
plt.subplot(7,5,x)
sns.countplot(df[i])
x=x+1
else:
plt.subplot(7,5,x)
sns.distplot(df[i])
x=x+1
plt.figure(figsize=(20,40))
x=1
for i in df.columns:
if(df[i].dtypes=="object"):
plt.subplot(7,5,x)
sns.countplot(df[i],hue=df.Attrition)
x=x+1
else:
plt.subplot(7,5,x)
sns.scatterplot(df[i],df['Attrition'])
x=x+1
sns.pairplot(df)
<seaborn.axisgrid.PairGrid at 0x2ead201f580>
df['Age'].unique()
array([41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 29, 31, 34, 28, 22, 53,
24, 21, 42, 44, 46, 39, 43, 50, 26, 48, 55, 45, 56, 23, 51, 40, 54,
58, 20, 25, 19, 57, 52, 47, 18, 60], dtype=int64)
df['Gender'].unique()
array(['Female', 'Male'], dtype=object)
df['Gender'].value_counts()
Male 882 Female 588 Name: Gender, dtype: int64
AG = pd.crosstab(df['Age'],df['Gender'])
AG
| Gender | Female | Male |
|---|---|---|
| Age | ||
| 18 | 4 | 4 |
| 19 | 4 | 5 |
| 20 | 6 | 5 |
| 21 | 6 | 7 |
| 22 | 5 | 11 |
| 23 | 1 | 13 |
| 24 | 11 | 15 |
| 25 | 6 | 20 |
| 26 | 14 | 25 |
| 27 | 22 | 26 |
| 28 | 12 | 36 |
| 29 | 29 | 39 |
| 30 | 26 | 34 |
| 31 | 28 | 41 |
| 32 | 23 | 38 |
| 33 | 24 | 34 |
| 34 | 33 | 44 |
| 35 | 27 | 51 |
| 36 | 29 | 40 |
| 37 | 18 | 32 |
| 38 | 27 | 31 |
| 39 | 16 | 26 |
| 40 | 24 | 33 |
| 41 | 15 | 25 |
| 42 | 16 | 30 |
| 43 | 14 | 18 |
| 44 | 10 | 23 |
| 45 | 17 | 24 |
| 46 | 15 | 18 |
| 47 | 14 | 10 |
| 48 | 7 | 12 |
| 49 | 12 | 12 |
| 50 | 9 | 21 |
| 51 | 7 | 12 |
| 52 | 7 | 11 |
| 53 | 10 | 9 |
| 54 | 15 | 3 |
| 55 | 4 | 18 |
| 56 | 7 | 7 |
| 57 | 0 | 4 |
| 58 | 6 | 8 |
| 59 | 6 | 4 |
| 60 | 2 | 3 |
from scipy.stats import chi2_contingency
chi_sqr, p_value, DOF, EXP = chi2_contingency(AG)
chi_sqr
53.537114950915246
p_value
0.10927801670328505
df['Gender'].unique()
array(['Female', 'Male'], dtype=object)
df['EducationField'].unique()
array(['Life Sciences', 'Other', 'Medical', 'Marketing',
'Technical Degree', 'Human Resources'], dtype=object)
df['Gender'].value_counts()
Male 882 Female 588 Name: Gender, dtype: int64
df['EducationField'].value_counts()
Life Sciences 606 Medical 464 Marketing 159 Technical Degree 132 Other 82 Human Resources 27 Name: EducationField, dtype: int64
GE = pd.crosstab(df['Gender'],df['EducationField'])
GE
| EducationField | Human Resources | Life Sciences | Marketing | Medical | Other | Technical Degree |
|---|---|---|---|---|---|---|
| Gender | ||||||
| Female | 8 | 240 | 69 | 190 | 29 | 52 |
| Male | 19 | 366 | 90 | 274 | 53 | 80 |
chi_sqr, p_value, DOF, EXP = chi2_contingency(GE)
chi_sqr
2.9414238793151797
p_value
0.7090162522843911
df['Age'].unique()
array([41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 29, 31, 34, 28, 22, 53,
24, 21, 42, 44, 46, 39, 43, 50, 26, 48, 55, 45, 56, 23, 51, 40, 54,
58, 20, 25, 19, 57, 52, 47, 18, 60], dtype=int64)
df['MonthlyIncome'].unique()
array([5993, 5130, 2090, ..., 9991, 5390, 4404], dtype=int64)
df['MonthlyIncome'].value_counts()
2342 4
6142 3
2610 3
2559 3
6347 3
..
4103 1
2705 1
6796 1
19717 1
10239 1
Name: MonthlyIncome, Length: 1349, dtype: int64
AMI = pd.crosstab(df['Age'],df['MonthlyIncome'])
AMI
| MonthlyIncome | 1009 | 1051 | 1052 | 1081 | 1091 | 1102 | 1118 | 1129 | 1200 | 1223 | 1232 | 1261 | 1274 | 1281 | 1359 | 1393 | 1416 | 1420 | 1483 | 1514 | 1555 | 1563 | 1569 | 1601 | 1611 | 1675 | 1702 | 1706 | 1790 | 1859 | 1878 | 1904 | 1951 | 2001 | 2007 | 2008 | 2011 | 2013 | 2014 | 2018 | 2022 | 2024 | 2028 | 2029 | 2033 | 2042 | 2044 | 2045 | 2058 | 2061 | ... | 19033 | 19038 | 19045 | 19049 | 19068 | 19081 | 19094 | 19141 | 19144 | 19161 | 19187 | 19189 | 19190 | 19197 | 19202 | 19232 | 19237 | 19246 | 19272 | 19328 | 19331 | 19392 | 19406 | 19419 | 19431 | 19436 | 19502 | 19513 | 19517 | 19537 | 19545 | 19566 | 19586 | 19613 | 19626 | 19627 | 19636 | 19658 | 19665 | 19701 | 19717 | 19740 | 19833 | 19845 | 19847 | 19859 | 19926 | 19943 | 19973 | 19999 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 18 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 19 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 25 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 28 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 29 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 30 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 32 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 33 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 34 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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43 rows × 1349 columns
chi_sqr, p_value, DOF, EXP = chi2_contingency(AMI)
chi_sqr
57003.01607044047
p_value
0.12517635416744993
final_df.corr()['MonthlyRate']
BusinessTravel_Non-Travel 0.015279 BusinessTravel_Travel_Frequently 0.000344 BusinessTravel_Travel_Rarely -0.010484 Department_Human Resources -0.024390 Department_Research & Development -0.005453 Department_Sales 0.016388 EducationField_Human Resources 0.009567 EducationField_Life Sciences 0.025545 EducationField_Marketing -0.011559 EducationField_Medical -0.001723 EducationField_Other -0.035606 EducationField_Technical Degree -0.004535 Gender_Female 0.041482 Gender_Male -0.041482 JobRole_Healthcare Representative 0.003829 JobRole_Human Resources -0.027470 JobRole_Laboratory Technician -0.016056 JobRole_Manager 0.031717 JobRole_Manufacturing Director 0.007711 JobRole_Research Director 0.025875 JobRole_Research Scientist -0.027008 JobRole_Sales Executive 0.011854 JobRole_Sales Representative -0.001200 MaritalStatus_Divorced -0.000227 MaritalStatus_Married -0.034689 MaritalStatus_Single 0.037260 Over18_Y NaN OverTime_No -0.021431 OverTime_Yes 0.021431 Age Group_Adult -0.018712 Age Group_Child 0.001694 Age Group_Old 0.018529 Age 0.028051 DailyRate -0.032182 DistanceFromHome 0.027473 Education -0.026084 EmployeeCount NaN EmployeeNumber 0.012648 EnvironmentSatisfaction 0.037600 HourlyRate -0.015297 JobInvolvement -0.016322 JobLevel 0.039563 JobSatisfaction 0.000644 MonthlyIncome 0.034814 MonthlyRate 1.000000 NumCompaniesWorked 0.017521 PercentSalaryHike -0.006429 PerformanceRating -0.009811 RelationshipSatisfaction -0.004085 StandardHours NaN StockOptionLevel -0.034323 TotalWorkingYears 0.026442 TrainingTimesLastYear 0.001467 WorkLifeBalance 0.007963 YearsAtCompany -0.023655 YearsInCurrentRole -0.012815 YearsSinceLastPromotion 0.001567 YearsWithCurrManager -0.036746 Attrition 0.015170 Name: MonthlyRate, dtype: float64
final_df
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | Attrition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | 1.153254 | -0.108350 | 0.726020 | 2.125136 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.421642 | -2.171982 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | 0.245834 | 1 |
| 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.660853 | -0.291719 | 1.488876 | -0.678049 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | -0.164511 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 0.806541 | 0 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | 0.246200 | -0.937654 | -1.674841 | 1.324226 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.550208 | 0.155707 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 | 1 |
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | 0.246200 | -0.763634 | 1.243211 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 0.252146 | -1.155935 | 0 |
| 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.660853 | -0.644858 | 0.325900 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.678774 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | 1.153254 | -0.835451 | -0.284329 | 0.523316 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.314873 | 0 |
| 1466 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | -1.567907 | 0.741140 | 1.004010 | 0.523316 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.293077 | 1.707500 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | 0.806541 | 0 |
| 1467 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.660853 | -0.076690 | -1.284418 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -0.678774 | -2.171982 | 0.338096 | -0.164613 | -0.615492 | -0.679146 | -0.314873 | 0 |
| 1468 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.660853 | -0.236474 | -0.150393 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.735447 | 0.155707 | -1.077862 | 0.325228 | 0.488900 | -0.679146 | 1.086895 | 0 |
| 1469 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | 0.246200 | -0.445978 | -0.574124 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 1.754054 | -0.491174 | -0.339394 | -0.368715 | -0.595227 | 0 |
1470 rows × 59 columns
nonCorrWithmonthlyrate = [column for column in final_df if abs(final_df[column].corr(final_df["MonthlyRate"])) < 0.01]
final_df1 = final_df.drop(nonCorrWithmonthlyrate, axis=1)
final_df1
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Sales | EducationField_Life Sciences | EducationField_Marketing | EducationField_Other | Gender_Female | Gender_Male | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | MonthlyIncome | MonthlyRate | NumCompaniesWorked | StandardHours | StockOptionLevel | TotalWorkingYears | YearsAtCompany | YearsInCurrentRole | YearsWithCurrManager | Attrition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | -0.108350 | 0.726020 | 2.125136 | 0.0 | -0.932014 | -0.421642 | -0.164613 | -0.063296 | 0.245834 | 1 |
| 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.291719 | 1.488876 | -0.678049 | 0.0 | 0.241988 | -0.164511 | 0.488508 | 0.764998 | 0.806541 | 0 |
| 2 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | -0.937654 | -1.674841 | 1.324226 | 0.0 | -0.932014 | -0.550208 | -1.144294 | -1.167687 | -1.155935 | 1 |
| 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | -0.763634 | 1.243211 | -0.678049 | 0.0 | -0.932014 | -0.421642 | 0.161947 | 0.764998 | -1.155935 | 0 |
| 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.644858 | 0.325900 | 2.525591 | 0.0 | 0.241988 | -0.678774 | -0.817734 | -0.615492 | -0.595227 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | -0.835451 | -0.284329 | 0.523316 | 0.0 | 0.241988 | 0.735447 | -0.327893 | -0.615492 | -0.314873 | 0 |
| 1466 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | 0.741140 | 1.004010 | 0.523316 | 0.0 | 0.241988 | -0.293077 | -0.001333 | 0.764998 | 0.806541 | 0 |
| 1467 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.076690 | -1.284418 | -0.678049 | 0.0 | 0.241988 | -0.678774 | -0.164613 | -0.615492 | -0.314873 | 0 |
| 1468 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.236474 | -0.150393 | -0.277594 | 0.0 | -0.932014 | 0.735447 | 0.325228 | 0.488900 | 1.086895 | 0 |
| 1469 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | -0.445978 | -0.574124 | -0.277594 | 0.0 | -0.932014 | -0.678774 | -0.491174 | -0.339394 | -0.595227 | 0 |
1470 rows × 42 columns
Final_df = final_df1.drop(["MonthlyRate"],axis=1)
Final_df
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Sales | EducationField_Life Sciences | EducationField_Marketing | EducationField_Other | Gender_Female | Gender_Male | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | MonthlyIncome | NumCompaniesWorked | StandardHours | StockOptionLevel | TotalWorkingYears | YearsAtCompany | YearsInCurrentRole | YearsWithCurrManager | Attrition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | -0.108350 | 2.125136 | 0.0 | -0.932014 | -0.421642 | -0.164613 | -0.063296 | 0.245834 | 1 |
| 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.291719 | -0.678049 | 0.0 | 0.241988 | -0.164511 | 0.488508 | 0.764998 | 0.806541 | 0 |
| 2 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | -0.937654 | 1.324226 | 0.0 | -0.932014 | -0.550208 | -1.144294 | -1.167687 | -1.155935 | 1 |
| 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | -0.763634 | -0.678049 | 0.0 | -0.932014 | -0.421642 | 0.161947 | 0.764998 | -1.155935 | 0 |
| 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.644858 | 2.525591 | 0.0 | 0.241988 | -0.678774 | -0.817734 | -0.615492 | -0.595227 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | -0.835451 | 0.523316 | 0.0 | 0.241988 | 0.735447 | -0.327893 | -0.615492 | -0.314873 | 0 |
| 1466 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | 0.741140 | 0.523316 | 0.0 | 0.241988 | -0.293077 | -0.001333 | 0.764998 | 0.806541 | 0 |
| 1467 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.076690 | -0.678049 | 0.0 | 0.241988 | -0.678774 | -0.164613 | -0.615492 | -0.314873 | 0 |
| 1468 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.236474 | -0.277594 | 0.0 | -0.932014 | 0.735447 | 0.325228 | 0.488900 | 1.086895 | 0 |
| 1469 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | -0.445978 | -0.277594 | 0.0 | -0.932014 | -0.678774 | -0.491174 | -0.339394 | -0.595227 | 0 |
1470 rows × 41 columns
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif = pd.DataFrame()
vif['Features'] = Final_df.columns
vif['VIF'] = [variance_inflation_factor(Final_df.values,i) for i in range(Final_df.shape[1])]
vif
| Features | VIF | |
|---|---|---|
| 0 | BusinessTravel_Non-Travel | 1.443079 |
| 1 | BusinessTravel_Travel_Rarely | 1.414884 |
| 2 | Department_Human Resources | 6.376036 |
| 3 | Department_Sales | 4.893546 |
| 4 | EducationField_Life Sciences | 1.187202 |
| 5 | EducationField_Marketing | 1.581020 |
| 6 | EducationField_Other | 1.096789 |
| 7 | Gender_Female | inf |
| 8 | Gender_Male | inf |
| 9 | JobRole_Human Resources | 6.412652 |
| 10 | JobRole_Laboratory Technician | 2.273979 |
| 11 | JobRole_Manager | 3.481272 |
| 12 | JobRole_Research Director | 2.353438 |
| 13 | JobRole_Research Scientist | 2.411984 |
| 14 | JobRole_Sales Executive | 3.993258 |
| 15 | MaritalStatus_Married | 1.854259 |
| 16 | MaritalStatus_Single | 3.062720 |
| 17 | Over18_Y | 0.000000 |
| 18 | OverTime_No | inf |
| 19 | OverTime_Yes | inf |
| 20 | Age Group_Adult | 43.488194 |
| 21 | Age Group_Old | 49.162061 |
| 22 | Age | 4.418960 |
| 23 | DailyRate | 1.030049 |
| 24 | DistanceFromHome | 1.028407 |
| 25 | Education | 1.092031 |
| 26 | EmployeeCount | NaN |
| 27 | EmployeeNumber | 1.026819 |
| 28 | EnvironmentSatisfaction | 1.041506 |
| 29 | HourlyRate | 1.019807 |
| 30 | JobInvolvement | 1.046943 |
| 31 | JobLevel | 14.048080 |
| 32 | MonthlyIncome | 18.039304 |
| 33 | NumCompaniesWorked | 1.286700 |
| 34 | StandardHours | NaN |
| 35 | StockOptionLevel | 1.915341 |
| 36 | TotalWorkingYears | 4.988296 |
| 37 | YearsAtCompany | 4.392647 |
| 38 | YearsInCurrentRole | 2.710913 |
| 39 | YearsWithCurrManager | 2.829019 |
| 40 | Attrition | 1.280857 |
featurestodrop = vif.loc[vif['VIF']>10]
droplist = featurestodrop['Features']
droplist = list(droplist)
len(droplist)
print(droplist)
['Gender_Female', 'Gender_Male', 'OverTime_No', 'OverTime_Yes', 'Age Group_Adult', 'Age Group_Old', 'JobLevel', 'MonthlyIncome']
df_Att= Final_df.drop(droplist,axis=1)
df_Att
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Sales | EducationField_Life Sciences | EducationField_Marketing | EducationField_Other | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | NumCompaniesWorked | StandardHours | StockOptionLevel | TotalWorkingYears | YearsAtCompany | YearsInCurrentRole | YearsWithCurrManager | Attrition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | 2.125136 | 0.0 | -0.932014 | -0.421642 | -0.164613 | -0.063296 | 0.245834 | 1 |
| 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.678049 | 0.0 | 0.241988 | -0.164511 | 0.488508 | 0.764998 | 0.806541 | 0 |
| 2 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | 1.324226 | 0.0 | -0.932014 | -0.550208 | -1.144294 | -1.167687 | -1.155935 | 1 |
| 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.678049 | 0.0 | -0.932014 | -0.421642 | 0.161947 | 0.764998 | -1.155935 | 0 |
| 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | 2.525591 | 0.0 | 0.241988 | -0.678774 | -0.817734 | -0.615492 | -0.595227 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | 0.523316 | 0.0 | 0.241988 | 0.735447 | -0.327893 | -0.615492 | -0.314873 | 0 |
| 1466 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.523316 | 0.0 | 0.241988 | -0.293077 | -0.001333 | 0.764998 | 0.806541 | 0 |
| 1467 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.678049 | 0.0 | 0.241988 | -0.678774 | -0.164613 | -0.615492 | -0.314873 | 0 |
| 1468 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.277594 | 0.0 | -0.932014 | 0.735447 | 0.325228 | 0.488900 | 1.086895 | 0 |
| 1469 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.277594 | 0.0 | -0.932014 | -0.678774 | -0.491174 | -0.339394 | -0.595227 | 0 |
1470 rows × 33 columns
x = df_Att
x
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Sales | EducationField_Life Sciences | EducationField_Marketing | EducationField_Other | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | NumCompaniesWorked | StandardHours | StockOptionLevel | TotalWorkingYears | YearsAtCompany | YearsInCurrentRole | YearsWithCurrManager | Attrition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | 2.125136 | 0.0 | -0.932014 | -0.421642 | -0.164613 | -0.063296 | 0.245834 | 1 |
| 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.678049 | 0.0 | 0.241988 | -0.164511 | 0.488508 | 0.764998 | 0.806541 | 0 |
| 2 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | 1.324226 | 0.0 | -0.932014 | -0.550208 | -1.144294 | -1.167687 | -1.155935 | 1 |
| 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.678049 | 0.0 | -0.932014 | -0.421642 | 0.161947 | 0.764998 | -1.155935 | 0 |
| 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | 2.525591 | 0.0 | 0.241988 | -0.678774 | -0.817734 | -0.615492 | -0.595227 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | 0.523316 | 0.0 | 0.241988 | 0.735447 | -0.327893 | -0.615492 | -0.314873 | 0 |
| 1466 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.523316 | 0.0 | 0.241988 | -0.293077 | -0.001333 | 0.764998 | 0.806541 | 0 |
| 1467 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.678049 | 0.0 | 0.241988 | -0.678774 | -0.164613 | -0.615492 | -0.314873 | 0 |
| 1468 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.277594 | 0.0 | -0.932014 | 0.735447 | 0.325228 | 0.488900 | 1.086895 | 0 |
| 1469 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.277594 | 0.0 | -0.932014 | -0.678774 | -0.491174 | -0.339394 | -0.595227 | 0 |
1470 rows × 33 columns
y = df[['MonthlyRate']]
y
| MonthlyRate | |
|---|---|
| 0 | 19479 |
| 1 | 24907 |
| 2 | 2396 |
| 3 | 23159 |
| 4 | 16632 |
| ... | ... |
| 1465 | 12290 |
| 1466 | 21457 |
| 1467 | 5174 |
| 1468 | 13243 |
| 1469 | 10228 |
1470 rows × 1 columns
xtrain,xtest,ytrain,ytest= train_test_split(x,y,test_size=0.25,random_state=25)
lin_model = LinearRegression()
lin_model.fit(xtrain,ytrain)
LinearRegression()
# Training data evaluation
ypredtrain = lin_model.predict(xtrain)
MAE = mean_absolute_error(ytrain,ypredtrain)
print('Mean absolute error:',MAE)
acc_linear = r2_score(ytrain,ypredtrain)
print('Training Accuracy:',acc_linear)
Mean absolute error: 6123.968239564429 Training Accuracy: 0.01751349297820326
# Testing data evaluation
ypredtest = lin_model.predict(xtest)
MAE = mean_absolute_error(ytest,ypredtest)
print('Mean absolute error:',MAE)
acc_linear = r2_score(ytest,ypredtest)
print('Testing Accuracy:',acc_linear)
Mean absolute error: 6164.817934782609 Testing Accuracy: -0.01650754483686856
lasso_model = Lasso()
lasso_model.fit(xtrain,ytrain)
Lasso()
# Training data evaluation
ypredtrain = lasso_model.predict(xtrain)
MAE = mean_absolute_error(ytrain,ypredtrain)
print('Mean absolute error:',MAE)
acc_lasso = r2_score(ytrain,ypredtrain)
print('Training Accuracy:',acc_lasso)
Mean absolute error: 6121.1571988063115 Training Accuracy: 0.01934535595164133
# Testing data evaluation
ypredtest = lasso_model.predict(xtest)
MAE = mean_absolute_error(ytest,ypredtest)
print('Mean absolute error:',MAE)
acc_lasso = r2_score(ytest,ypredtest)
print('Testing Accuracy:',acc_lasso)
Mean absolute error: 6189.8646400047155 Testing Accuracy: -0.02333262771485556
ridge_model = Ridge()
ridge_model.fit(xtrain,ytrain)
Ridge()
# Training data evaluation
ypredtrain = ridge_model.predict(xtrain)
Mean_absolute_error = mean_absolute_error(ytrain,ypredtrain)
print('Mean_absolute_error:',Mean_absolute_error)
acc_ridge = r2_score(ytrain,ypredtrain)
print('Training Accuracy:',acc_ridge)
Mean_absolute_error: 6121.036816827892 Training Accuracy: 0.01934582713731403
# Testing data evaluation
ypredtest = ridge_model.predict(xtest)
Mean_absolute_error = mean_absolute_error(ytest,ypredtest)
print('Mean_absolute_error:',Mean_absolute_error)
acc_ridge = r2_score(ytest,ypredtest)
print('Testing Accuracy:',acc_ridge)
Mean_absolute_error: 6190.905298336976 Testing Accuracy: -0.023655552374451494
from sklearn.neighbors import KNeighborsRegressor
KNN_model = KNeighborsRegressor()
KNN_model.fit(xtrain,ytrain)
KNeighborsRegressor()
# Training data evaluation
ypredtrain = KNN_model.predict(xtrain)
acc_KNR = r2_score(ytrain,ypredtrain)
print('Training accuracy:',acc_KNR )
Training accuracy: 0.21360805483649958
# Testing data evaluation
ypredtest = KNN_model.predict(xtest)
# ypredtest
acc_KNR = r2_score(ytest,ypredtest)
print('Testing accuracy:',acc_KNR )
Testing accuracy: -0.15435468903713745
from sklearn.tree import DecisionTreeRegressor, plot_tree
dt_reg = DecisionTreeRegressor()
dt_reg.fit(xtrain, ytrain)
DecisionTreeRegressor()
ypredtrain = dt_reg.predict(xtrain)
mse = mean_squared_error(ytrain, ypredtrain)
print("MSE :",mse)
rmse = np.sqrt(mse)
print("RMSE :",rmse)
acc_DT = r2_score(ytrain, ypredtrain)
print("Training accuracy :",acc_DT)
MSE : 0.0 RMSE : 0.0 Training accuracy : 1.0
ypred = dt_reg.predict(xtest)
mse = mean_squared_error(ytest, ypred)
print("MSE :",mse)
rmse = np.sqrt(mse)
print("RMSE :",rmse)
acc_DT = r2_score(ytest, ypred)
print("Testing accuracy :",acc_DT)
MSE : 101312354.08152173 RMSE : 10065.403821085458 Testing accuracy : -1.0247599731746173
# dt_reg = DecisionTreeRegressor()
# hyp_grid = {'criterion':['mse','mae'],
# 'max_depth':np.arange(3,15),
# 'min_samples_split':np.arange(2,20),
# 'min_samples_leaf':np.arange(2,15)}
# gscv_dt_reg = GridSearchCV(dt_reg,hyp_grid,cv=5)
# gscv_dt_reg.fit(xtrain,ytrain)
# gscv_dt_reg.best_estimator_
# dt_reg = DecisionTreeRegressor(criterion='mae', max_depth=3, min_samples_leaf=2, min_samples_split=5)
# dt_reg.fit(xtrain, ytrain)
# ypredtrain = dt_reg.predict(xtrain)
# mse = mean_squared_error(ytrain, ypredtrain)
# print("MSE :",mse)
# rmse = np.sqrt(mse)
# print("RMSE :",rmse)
# acc_DT = r2_score(ytrain, ypredtrain)
# print("Training accuracy :",acc_DT)
# ypred = dt_reg.predict(xtest)
# mse = mean_squared_error(ytest, ypred)
# print("MSE :",mse)
# rmse = np.sqrt(mse)
# print("RMSE :",rmse)
# acc_DT = r2_score(ytest, ypred)
# print("Testing accuracy :",acc_DT)
# plt.figure(figsize=(200,150))
# tree = plot_tree(dt_reg,feature_names=x.columns,filled=True)
# plt.savefig('dt_reg.png')
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,recall_score,precision_score,f1_score
final_df
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | Attrition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | 1.153254 | -0.108350 | 0.726020 | 2.125136 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.421642 | -2.171982 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | 0.245834 | 1 |
| 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.660853 | -0.291719 | 1.488876 | -0.678049 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | -0.164511 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 0.806541 | 0 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | 0.246200 | -0.937654 | -1.674841 | 1.324226 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.550208 | 0.155707 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 | 1 |
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | 0.246200 | -0.763634 | 1.243211 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 0.252146 | -1.155935 | 0 |
| 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.660853 | -0.644858 | 0.325900 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.678774 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | 1.153254 | -0.835451 | -0.284329 | 0.523316 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.314873 | 0 |
| 1466 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | -1.567907 | 0.741140 | 1.004010 | 0.523316 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.293077 | 1.707500 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | 0.806541 | 0 |
| 1467 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.660853 | -0.076690 | -1.284418 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -0.678774 | -2.171982 | 0.338096 | -0.164613 | -0.615492 | -0.679146 | -0.314873 | 0 |
| 1468 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.660853 | -0.236474 | -0.150393 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.735447 | 0.155707 | -1.077862 | 0.325228 | 0.488900 | -0.679146 | 1.086895 | 0 |
| 1469 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | 0.246200 | -0.445978 | -0.574124 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 1.754054 | -0.491174 | -0.339394 | -0.368715 | -0.595227 | 0 |
1470 rows × 59 columns
Final_df_class = final_df.drop(["Attrition"],axis=1)
Final_df_class
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | 1.153254 | -0.108350 | 0.726020 | 2.125136 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.421642 | -2.171982 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | 0.245834 |
| 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.660853 | -0.291719 | 1.488876 | -0.678049 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | -0.164511 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 0.806541 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | 0.246200 | -0.937654 | -1.674841 | 1.324226 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.550208 | 0.155707 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 |
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | 0.246200 | -0.763634 | 1.243211 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 0.252146 | -1.155935 |
| 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.660853 | -0.644858 | 0.325900 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.678774 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | 1.153254 | -0.835451 | -0.284329 | 0.523316 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.314873 |
| 1466 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | -1.567907 | 0.741140 | 1.004010 | 0.523316 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.293077 | 1.707500 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | 0.806541 |
| 1467 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.660853 | -0.076690 | -1.284418 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -0.678774 | -2.171982 | 0.338096 | -0.164613 | -0.615492 | -0.679146 | -0.314873 |
| 1468 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.660853 | -0.236474 | -0.150393 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.735447 | 0.155707 | -1.077862 | 0.325228 | 0.488900 | -0.679146 | 1.086895 |
| 1469 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | 0.246200 | -0.445978 | -0.574124 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 1.754054 | -0.491174 | -0.339394 | -0.368715 | -0.595227 |
1470 rows × 58 columns
x = Final_df_class
x
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | 1.153254 | -0.108350 | 0.726020 | 2.125136 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.421642 | -2.171982 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | 0.245834 |
| 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.660853 | -0.291719 | 1.488876 | -0.678049 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | -0.164511 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 0.806541 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | 0.246200 | -0.937654 | -1.674841 | 1.324226 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.550208 | 0.155707 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 |
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | 0.246200 | -0.763634 | 1.243211 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 0.252146 | -1.155935 |
| 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.660853 | -0.644858 | 0.325900 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.678774 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | 1.153254 | -0.835451 | -0.284329 | 0.523316 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.314873 |
| 1466 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | -1.567907 | 0.741140 | 1.004010 | 0.523316 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.293077 | 1.707500 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | 0.806541 |
| 1467 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.660853 | -0.076690 | -1.284418 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -0.678774 | -2.171982 | 0.338096 | -0.164613 | -0.615492 | -0.679146 | -0.314873 |
| 1468 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.660853 | -0.236474 | -0.150393 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.735447 | 0.155707 | -1.077862 | 0.325228 | 0.488900 | -0.679146 | 1.086895 |
| 1469 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | 0.246200 | -0.445978 | -0.574124 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 1.754054 | -0.491174 | -0.339394 | -0.368715 | -0.595227 |
1470 rows × 58 columns
y = df[['Attrition']]
y
| Attrition | |
|---|---|
| 0 | 1 |
| 1 | 0 |
| 2 | 1 |
| 3 | 0 |
| 4 | 0 |
| ... | ... |
| 1465 | 0 |
| 1466 | 0 |
| 1467 | 0 |
| 1468 | 0 |
| 1469 | 0 |
1470 rows × 1 columns
xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.25,random_state=25,stratify=y)
log_model = LogisticRegression()
log_model.fit(xtrain,ytrain)
LogisticRegression()
# Training data evaluation
ypredtrain = log_model.predict(xtrain)
Accuracy_log = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_log)
confusionmatrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion matrix: \n',confusionmatrix)
Precision = precision_score(ytrain,ypredtrain)
print('Precision:',Precision)
Recall = recall_score(ytrain,ypredtrain)
print('Recall:',Recall)
F1score = f1_score(ytrain,ypredtrain)
print('F1score:',F1score)
Training Accuracy: 0.8892921960072595 Confusion matrix: [[903 21] [101 77]] Precision: 0.7857142857142857 Recall: 0.43258426966292135 F1score: 0.5579710144927537
# Testing data evaluation
ypredtest = log_model.predict(xtest)
Accuracy_log = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_log)
confusionmatrix = confusion_matrix(ytest,ypredtest)
print('Confusion matrix: \n',confusionmatrix)
Precision = precision_score(ytest,ypredtest)
print('Precision:',Precision)
Recall = recall_score(ytest,ypredtest)
print('Recall:',Recall)
F1score = f1_score(ytest,ypredtest)
print('F1score:',F1score)
Testing Accuracy: 0.8831521739130435 Confusion matrix: [[297 12] [ 31 28]] Precision: 0.7 Recall: 0.4745762711864407 F1score: 0.5656565656565656
from sklearn.neighbors import KNeighborsClassifier
Knn_model = KNeighborsClassifier()
Knn_model.fit(xtrain,ytrain)
KNeighborsClassifier()
# Training data evaluation
ypredtrain = Knn_model.predict(xtrain)
Accuracy_KNN = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_KNN)
Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix:\n',Confusion_matrix)
Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report:\n',Classification_report)
Training Accuracy: 0.8593466424682396
Confusion_matrix:
[[915 9]
[146 32]]
Classification_report:
precision recall f1-score support
0 0.86 0.99 0.92 924
1 0.78 0.18 0.29 178
accuracy 0.86 1102
macro avg 0.82 0.59 0.61 1102
weighted avg 0.85 0.86 0.82 1102
# Testing data evaluation
ypredtest = Knn_model.predict(xtest)
# ypredtest
Accuracy_KNN = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_KNN)
Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix:\n',Confusion_matrix)
Classification_report = classification_report(ytest,ypredtest)
print('Classification_report:\n',Classification_report)
Testing Accuracy: 0.8315217391304348
Confusion_matrix:
[[302 7]
[ 55 4]]
Classification_report:
precision recall f1-score support
0 0.85 0.98 0.91 309
1 0.36 0.07 0.11 59
accuracy 0.83 368
macro avg 0.60 0.52 0.51 368
weighted avg 0.77 0.83 0.78 368
knn_model = KNeighborsClassifier()
hyp_grid = {'n_neighbors':np.arange(3,40),'p':[1,2]}
gscv_knn_model = GridSearchCV(knn_model,hyp_grid,cv=5)
gscv_knn_model.fit(xtrain,ytrain)
gscv_knn_model.best_estimator_
KNeighborsClassifier(n_neighbors=3, p=1)
Knn_model = KNeighborsClassifier(n_neighbors=7, p=1)
Knn_model.fit(xtrain,ytrain)
KNeighborsClassifier(n_neighbors=7, p=1)
# Training data evaluation
ypredtrain = Knn_model.predict(xtrain)
Accuracy_KNN = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_KNN)
Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix:\n',Confusion_matrix)
Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report:\n',Classification_report)
Training Accuracy: 0.8647912885662432
Confusion_matrix:
[[919 5]
[144 34]]
Classification_report:
precision recall f1-score support
0 0.86 0.99 0.93 924
1 0.87 0.19 0.31 178
accuracy 0.86 1102
macro avg 0.87 0.59 0.62 1102
weighted avg 0.87 0.86 0.83 1102
# Testing data evaluation
ypredtest = Knn_model.predict(xtest)
# ypredtest
Accuracy_KNN = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_KNN)
Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix:\n',Confusion_matrix)
Classification_report = classification_report(ytest,ypredtest)
print('Classification_report:\n',Classification_report)
Testing Accuracy: 0.8478260869565217
Confusion_matrix:
[[306 3]
[ 53 6]]
Classification_report:
precision recall f1-score support
0 0.85 0.99 0.92 309
1 0.67 0.10 0.18 59
accuracy 0.85 368
macro avg 0.76 0.55 0.55 368
weighted avg 0.82 0.85 0.80 368
from sklearn.naive_bayes import BernoulliNB
baysian_model = BernoulliNB()
baysian_model.fit(xtrain,ytrain)
BernoulliNB()
# Training data evaluation
ypredtrain = baysian_model.predict(xtrain)
Accuracy_bay = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_bay)
Training Accuracy: 0.8303085299455535
# Testing data evaluation
ypredtest = baysian_model.predict(xtest)
Accuracy_bay = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_bay)
Testing Accuracy: 0.8478260869565217
from sklearn.tree import DecisionTreeClassifier, plot_tree
dt_model = DecisionTreeClassifier()
dt_model.fit(xtrain,ytrain)
DecisionTreeClassifier()
# Training data evaluation
ypredtrain = dt_model.predict(xtrain)
Accuracy_DTC = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_DTC)
Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)
Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)
Training Accuracy: 1.0
Confusion_matrix:
[[924 0]
[ 0 178]]
Classification_report:
precision recall f1-score support
0 1.00 1.00 1.00 924
1 1.00 1.00 1.00 178
accuracy 1.00 1102
macro avg 1.00 1.00 1.00 1102
weighted avg 1.00 1.00 1.00 1102
# Testing data evaluation
ypredtest = dt_model.predict(xtest)
Accuracy_DTC = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_DTC)
Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)
Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)
Testing Accuracy: 0.7961956521739131
Confusion_matrix:
[[271 38]
[ 37 22]]
Classification_report:
precision recall f1-score support
0 0.88 0.88 0.88 309
1 0.37 0.37 0.37 59
accuracy 0.80 368
macro avg 0.62 0.62 0.62 368
weighted avg 0.80 0.80 0.80 368
dt_model = DecisionTreeClassifier()
hyp_grid = {'criterion':['gini','entropy'],
'max_depth': np.arange(3,15),
'min_samples_split':np.arange(2,20),
'min_samples_leaf':np.arange(1,15)}
gscv_dt_model = GridSearchCV(dt_model,hyp_grid,cv=5)
gscv_dt_model.fit(xtrain,ytrain)
GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),
param_grid={'criterion': ['gini', 'entropy'],
'max_depth': array([ 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]),
'min_samples_leaf': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]),
'min_samples_split': array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19])})
gscv_dt_model.best_estimator_
DecisionTreeClassifier(max_depth=3, min_samples_leaf=6)
dt_model = DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=6,min_samples_split=5)
dt_model.fit(xtrain,ytrain)
DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=6,
min_samples_split=5)
# Training data evaluation
ypredtrain = dt_model.predict(xtrain)
Accuracy_DTC = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_DTC)
Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)
Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)
Training Accuracy: 0.8584392014519057
Confusion_matrix:
[[911 13]
[143 35]]
Classification_report:
precision recall f1-score support
0 0.86 0.99 0.92 924
1 0.73 0.20 0.31 178
accuracy 0.86 1102
macro avg 0.80 0.59 0.62 1102
weighted avg 0.84 0.86 0.82 1102
# Testing data evaluation
ypredtest = dt_model.predict(xtest)
Accuracy_DTC = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_DTC)
Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)
Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)
Testing Accuracy: 0.8614130434782609
Confusion_matrix:
[[304 5]
[ 46 13]]
Classification_report:
precision recall f1-score support
0 0.87 0.98 0.92 309
1 0.72 0.22 0.34 59
accuracy 0.86 368
macro avg 0.80 0.60 0.63 368
weighted avg 0.85 0.86 0.83 368
plt.figure(figsize=(200,150))
tree = plot_tree(dt_model,feature_names=x.columns,class_names=['0','1'],filled=True)
plt.savefig('dt_afterhypt.png')
from sklearn.svm import SVC
svc_model = SVC()
svc_model.fit(xtrain,ytrain)
SVC()
# Training data evaluation
ypredtrain = svc_model.predict(xtrain)
# ypredtest
Accuracy_SVM = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_SVM)
Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)
Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)
Training Accuracy: 0.911978221415608
Confusion_matrix:
[[924 0]
[ 97 81]]
Classification_report:
precision recall f1-score support
0 0.90 1.00 0.95 924
1 1.00 0.46 0.63 178
accuracy 0.91 1102
macro avg 0.95 0.73 0.79 1102
weighted avg 0.92 0.91 0.90 1102
# Testing data evaluation
ypredtest = svc_model.predict(xtest)
# ypredtest
Accuracy_SVM = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_SVM)
Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)
Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)
Testing Accuracy: 0.8722826086956522
Confusion_matrix:
[[308 1]
[ 46 13]]
Classification_report:
precision recall f1-score support
0 0.87 1.00 0.93 309
1 0.93 0.22 0.36 59
accuracy 0.87 368
macro avg 0.90 0.61 0.64 368
weighted avg 0.88 0.87 0.84 368
svc_model = SVC()
hyp_grid = {'C':np.arange(0,50),
'kernel' : ['linear', 'poly', 'rbf', 'sigmoid']}
gscv_scv_model = GridSearchCV(svc_model,hyp_grid,cv=5)
gscv_scv_model.fit(xtrain,ytrain)
GridSearchCV(cv=5, estimator=SVC(),
param_grid={'C': array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),
'kernel': ['linear', 'poly', 'rbf', 'sigmoid']})
gscv_scv_model.best_estimator_
SVC(C=1, kernel='linear')
svc_model = SVC(C=1, kernel='linear')
svc_model.fit(xtrain,ytrain)
SVC(C=1, kernel='linear')
# Training data evaluation
ypredtrain = svc_model.predict(xtrain)
# ypredtest
Accuracy_SVM = accuracy_score(ytrain,ypredtrain)
print('Training Accuracy:',Accuracy_SVM)
Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)
Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)
Training Accuracy: 0.8920145190562614
Confusion_matrix:
[[913 11]
[108 70]]
Classification_report:
precision recall f1-score support
0 0.89 0.99 0.94 924
1 0.86 0.39 0.54 178
accuracy 0.89 1102
macro avg 0.88 0.69 0.74 1102
weighted avg 0.89 0.89 0.87 1102
# Testing data evaluation
ypredtest = svc_model.predict(xtest)
# ypredtest
Accuracy_SVM = accuracy_score(ytest,ypredtest)
print('Testing Accuracy:',Accuracy_SVM)
Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)
Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)
Testing Accuracy: 0.8967391304347826
Confusion_matrix:
[[300 9]
[ 29 30]]
Classification_report:
precision recall f1-score support
0 0.91 0.97 0.94 309
1 0.77 0.51 0.61 59
accuracy 0.90 368
macro avg 0.84 0.74 0.78 368
weighted avg 0.89 0.90 0.89 368
import tensorflow as tf
import keras
from keras import Sequential
from keras.layers import Dense , Dropout
std_scaler = StandardScaler()
xtrain_scaled = std_scaler.fit_transform(xtrain)
xtest_scaled = std_scaler.fit_transform(xtest)
xtrain_scaled
array([[-0.33299714, 2.04892977, -1.55639335, ..., 1.33370758,
1.19896149, 0.80217198],
[-0.33299714, 2.04892977, -1.55639335, ..., -0.6096661 ,
-0.36948053, -0.60635817],
[ 3.00302877, -0.48805968, -1.55639335, ..., -0.33204129,
-0.36948053, -0.60635817],
...,
[-0.33299714, -0.48805968, 0.6425111 , ..., -0.6096661 ,
-0.05579213, -0.60635817],
[-0.33299714, -0.48805968, 0.6425111 , ..., -1.16491573,
-0.68316894, -1.16977023],
[-0.33299714, -0.48805968, 0.6425111 , ..., -0.33204129,
-0.68316894, -0.04294611]])
xtest_scaled
array([[-0.34921515, -0.46316461, 0.63185405, ..., 0.72604239,
-0.66802355, 1.09633725],
[-0.34921515, -0.46316461, 0.63185405, ..., 1.26965094,
2.04336617, 0.54328433],
[-0.34921515, 2.15905962, -1.58264396, ..., -0.08937043,
-0.66802355, -0.56282152],
...,
[-0.34921515, -0.46316461, 0.63185405, ..., 0.72604239,
-0.36675803, 0.81981079],
[-0.34921515, -0.46316461, 0.63185405, ..., -0.08937043,
-0.36675803, -0.56282152],
[-0.34921515, -0.46316461, 0.63185405, ..., 1.54145521,
-0.06549251, 1.37286372]])
nn = Sequential()
# Create input layer and 1st Hidden layer
nn.add(Dense(units = 6,activation='relu',kernel_initializer='he_uniform',input_dim=(58)))
# Add 1st Dropout layer
nn.add(Dropout(rate=0.5))
# Add 2nd Hidden layer
nn.add(Dense(units = 6,activation='relu',kernel_initializer='he_uniform'))
# Add 2nd Dropout layer
nn.add(Dropout(rate=0.5))
# Add output layer
nn.add(Dense(units=1 ,activation='sigmoid', kernel_initializer='glorot_uniform'))
# Compile
nn.compile(optimizer='Adam',loss='binary_crossentropy' ,metrics=['accuracy','Recall','Precision'])
# Training
model = nn.fit(xtrain_scaled,ytrain,validation_split=0.3,epochs=200,batch_size=20)
Epoch 1/200 39/39 [==============================] - 1s 11ms/step - loss: 1.1075 - accuracy: 0.5772 - recall: 0.3629 - precision: 0.1541 - val_loss: 0.7224 - val_accuracy: 0.5227 - val_recall: 0.4630 - val_precision: 0.1623 Epoch 2/200 39/39 [==============================] - 0s 3ms/step - loss: 0.9319 - accuracy: 0.6446 - recall: 0.3468 - precision: 0.1822 - val_loss: 0.6553 - val_accuracy: 0.6012 - val_recall: 0.3333 - val_precision: 0.1579 Epoch 3/200 39/39 [==============================] - 0s 3ms/step - loss: 0.8673 - accuracy: 0.6485 - recall: 0.2339 - precision: 0.1415 - val_loss: 0.5992 - val_accuracy: 0.7130 - val_recall: 0.2407 - val_precision: 0.1940 Epoch 4/200 39/39 [==============================] - 0s 3ms/step - loss: 0.7630 - accuracy: 0.7082 - recall: 0.3468 - precision: 0.2299 - val_loss: 0.5626 - val_accuracy: 0.7704 - val_recall: 0.1667 - val_precision: 0.2250 Epoch 5/200 39/39 [==============================] - 0s 3ms/step - loss: 0.7281 - accuracy: 0.7160 - recall: 0.2903 - precision: 0.2156 - val_loss: 0.5348 - val_accuracy: 0.8006 - val_recall: 0.1481 - val_precision: 0.2857 Epoch 6/200 39/39 [==============================] - 0s 3ms/step - loss: 0.6799 - accuracy: 0.7276 - recall: 0.2177 - precision: 0.1929 - val_loss: 0.5162 - val_accuracy: 0.8097 - val_recall: 0.0741 - val_precision: 0.2353 Epoch 7/200 39/39 [==============================] - 0s 2ms/step - loss: 0.5909 - accuracy: 0.7639 - recall: 0.2742 - precision: 0.2698 - val_loss: 0.5023 - val_accuracy: 0.8278 - val_recall: 0.0556 - val_precision: 0.3333 Epoch 8/200 39/39 [==============================] - 0s 2ms/step - loss: 0.6242 - accuracy: 0.7328 - recall: 0.1290 - precision: 0.1404 - val_loss: 0.4918 - val_accuracy: 0.8308 - val_recall: 0.0185 - val_precision: 0.2500 Epoch 9/200 39/39 [==============================] - 0s 3ms/step - loss: 0.5828 - accuracy: 0.7795 - recall: 0.1532 - precision: 0.2262 - val_loss: 0.4836 - val_accuracy: 0.8338 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 10/200 39/39 [==============================] - 0s 3ms/step - loss: 0.5740 - accuracy: 0.7808 - recall: 0.1290 - precision: 0.2078 - val_loss: 0.4778 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 11/200 39/39 [==============================] - 0s 4ms/step - loss: 0.5767 - accuracy: 0.7562 - recall: 0.0403 - precision: 0.0676 - val_loss: 0.4719 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 12/200 39/39 [==============================] - 0s 6ms/step - loss: 0.5269 - accuracy: 0.7847 - recall: 0.0726 - precision: 0.1500 - val_loss: 0.4663 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 13/200 39/39 [==============================] - 0s 6ms/step - loss: 0.5441 - accuracy: 0.7873 - recall: 0.0887 - precision: 0.1774 - val_loss: 0.4636 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 14/200 39/39 [==============================] - 0s 4ms/step - loss: 0.5270 - accuracy: 0.7938 - recall: 0.0645 - precision: 0.1569 - val_loss: 0.4602 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 15/200 39/39 [==============================] - 0s 3ms/step - loss: 0.5227 - accuracy: 0.7938 - recall: 0.0403 - precision: 0.1111 - val_loss: 0.4565 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 16/200 39/39 [==============================] - 0s 3ms/step - loss: 0.5335 - accuracy: 0.8042 - recall: 0.0565 - precision: 0.1707 - val_loss: 0.4530 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 17/200 39/39 [==============================] - 0s 3ms/step - loss: 0.5046 - accuracy: 0.8093 - recall: 0.0484 - precision: 0.1714 - val_loss: 0.4496 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 18/200 39/39 [==============================] - 0s 6ms/step - loss: 0.5372 - accuracy: 0.8132 - recall: 0.0242 - precision: 0.1154 - val_loss: 0.4475 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 19/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4878 - accuracy: 0.8249 - recall: 0.0484 - precision: 0.2609 - val_loss: 0.4456 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 20/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4781 - accuracy: 0.8262 - recall: 0.0484 - precision: 0.2727 - val_loss: 0.4433 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 21/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4869 - accuracy: 0.8236 - recall: 0.0323 - precision: 0.2000 - val_loss: 0.4410 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 22/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4939 - accuracy: 0.8171 - recall: 0.0242 - precision: 0.1304 - val_loss: 0.4395 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 23/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4821 - accuracy: 0.8275 - recall: 0.0081 - precision: 0.0909 - val_loss: 0.4376 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 24/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4705 - accuracy: 0.8353 - recall: 0.0565 - precision: 0.4118 - val_loss: 0.4344 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 25/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4751 - accuracy: 0.8301 - recall: 0.0161 - precision: 0.1818 - val_loss: 0.4323 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 26/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4503 - accuracy: 0.8327 - recall: 0.0323 - precision: 0.3077 - val_loss: 0.4311 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 27/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4499 - accuracy: 0.8327 - recall: 0.0161 - precision: 0.2222 - val_loss: 0.4291 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 28/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4429 - accuracy: 0.8366 - recall: 0.0161 - precision: 0.3333 - val_loss: 0.4281 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 29/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4462 - accuracy: 0.8405 - recall: 0.0403 - precision: 0.5556 - val_loss: 0.4262 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 30/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4584 - accuracy: 0.8405 - recall: 0.0565 - precision: 0.5385 - val_loss: 0.4255 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 31/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4498 - accuracy: 0.8418 - recall: 0.0242 - precision: 0.7500 - val_loss: 0.4238 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 32/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4550 - accuracy: 0.8405 - recall: 0.0161 - precision: 0.6667 - val_loss: 0.4235 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 33/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4276 - accuracy: 0.8431 - recall: 0.0484 - precision: 0.6667 - val_loss: 0.4226 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 34/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4142 - accuracy: 0.8418 - recall: 0.0323 - precision: 0.6667 - val_loss: 0.4196 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 35/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4368 - accuracy: 0.8457 - recall: 0.0565 - precision: 0.7778 - val_loss: 0.4175 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 36/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4422 - accuracy: 0.8431 - recall: 0.0484 - precision: 0.6667 - val_loss: 0.4148 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 37/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4367 - accuracy: 0.8431 - recall: 0.0565 - precision: 0.6364 - val_loss: 0.4140 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 38/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4281 - accuracy: 0.8392 - recall: 0.0242 - precision: 0.5000 - val_loss: 0.4123 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 39/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4324 - accuracy: 0.8457 - recall: 0.0645 - precision: 0.7273 - val_loss: 0.4120 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 40/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4100 - accuracy: 0.8482 - recall: 0.0887 - precision: 0.7333 - val_loss: 0.4104 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 41/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4130 - accuracy: 0.8418 - recall: 0.0484 - precision: 0.6000 - val_loss: 0.4075 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 42/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4193 - accuracy: 0.8457 - recall: 0.0645 - precision: 0.7273 - val_loss: 0.4081 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 43/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4088 - accuracy: 0.8457 - recall: 0.0565 - precision: 0.7778 - val_loss: 0.4081 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 44/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4093 - accuracy: 0.8482 - recall: 0.0887 - precision: 0.7333 - val_loss: 0.4078 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 45/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4141 - accuracy: 0.8470 - recall: 0.0645 - precision: 0.8000 - val_loss: 0.4059 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 46/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4132 - accuracy: 0.8495 - recall: 0.0726 - precision: 0.9000 - val_loss: 0.4064 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 47/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4133 - accuracy: 0.8534 - recall: 0.0968 - precision: 0.9231 - val_loss: 0.4045 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 48/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4055 - accuracy: 0.8521 - recall: 0.0887 - precision: 0.9167 - val_loss: 0.4013 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 49/200 39/39 [==============================] - 0s 4ms/step - loss: 0.4063 - accuracy: 0.8508 - recall: 0.1210 - precision: 0.7143 - val_loss: 0.3990 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 50/200 39/39 [==============================] - 0s 4ms/step - loss: 0.3968 - accuracy: 0.8547 - recall: 0.1048 - precision: 0.9286 - val_loss: 0.3972 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 51/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4007 - accuracy: 0.8534 - recall: 0.1210 - precision: 0.7895 - val_loss: 0.3947 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 52/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3868 - accuracy: 0.8586 - recall: 0.1532 - precision: 0.8261 - val_loss: 0.3932 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 53/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3915 - accuracy: 0.8612 - recall: 0.1613 - precision: 0.8696 - val_loss: 0.3899 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 54/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4005 - accuracy: 0.8547 - recall: 0.1210 - precision: 0.8333 - val_loss: 0.3903 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 55/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4007 - accuracy: 0.8521 - recall: 0.1129 - precision: 0.7778 - val_loss: 0.3876 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 56/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3969 - accuracy: 0.8495 - recall: 0.1129 - precision: 0.7000 - val_loss: 0.3900 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 57/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3953 - accuracy: 0.8495 - recall: 0.1290 - precision: 0.6667 - val_loss: 0.3907 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 58/200 39/39 [==============================] - 0s 3ms/step - loss: 0.4006 - accuracy: 0.8573 - recall: 0.1694 - precision: 0.7500 - val_loss: 0.3870 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 59/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3869 - accuracy: 0.8508 - recall: 0.1210 - precision: 0.7143 - val_loss: 0.3869 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 60/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3982 - accuracy: 0.8482 - recall: 0.1129 - precision: 0.6667 - val_loss: 0.3870 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 61/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3853 - accuracy: 0.8599 - recall: 0.1371 - precision: 0.9444 - val_loss: 0.3850 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 62/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3730 - accuracy: 0.8651 - recall: 0.1694 - precision: 0.9545 - val_loss: 0.3804 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 63/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3722 - accuracy: 0.8638 - recall: 0.1855 - precision: 0.8519 - val_loss: 0.3786 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 64/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3921 - accuracy: 0.8534 - recall: 0.1371 - precision: 0.7391 - val_loss: 0.3807 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 65/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3854 - accuracy: 0.8534 - recall: 0.1371 - precision: 0.7391 - val_loss: 0.3806 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 66/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3922 - accuracy: 0.8508 - recall: 0.1210 - precision: 0.7143 - val_loss: 0.3807 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 67/200 39/39 [==============================] - 0s 6ms/step - loss: 0.3642 - accuracy: 0.8625 - recall: 0.1935 - precision: 0.8000 - val_loss: 0.3798 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 68/200 39/39 [==============================] - 0s 6ms/step - loss: 0.3645 - accuracy: 0.8586 - recall: 0.1532 - precision: 0.8261 - val_loss: 0.3835 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 69/200 39/39 [==============================] - 0s 4ms/step - loss: 0.3754 - accuracy: 0.8495 - recall: 0.1048 - precision: 0.7222 - val_loss: 0.3848 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 70/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3921 - accuracy: 0.8560 - recall: 0.1613 - precision: 0.7407 - val_loss: 0.3828 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 71/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3836 - accuracy: 0.8560 - recall: 0.1694 - precision: 0.7241 - val_loss: 0.3817 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 72/200 39/39 [==============================] - 0s 4ms/step - loss: 0.3719 - accuracy: 0.8547 - recall: 0.1210 - precision: 0.8333 - val_loss: 0.3813 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 73/200 39/39 [==============================] - 0s 4ms/step - loss: 0.3679 - accuracy: 0.8690 - recall: 0.2339 - precision: 0.8286 - val_loss: 0.3818 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 74/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3696 - accuracy: 0.8677 - recall: 0.2016 - precision: 0.8929 - val_loss: 0.3781 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 75/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3785 - accuracy: 0.8521 - recall: 0.1371 - precision: 0.7083 - val_loss: 0.3808 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 76/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3670 - accuracy: 0.8638 - recall: 0.1855 - precision: 0.8519 - val_loss: 0.3803 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 77/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3632 - accuracy: 0.8573 - recall: 0.1774 - precision: 0.7333 - val_loss: 0.3781 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 78/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3725 - accuracy: 0.8625 - recall: 0.1774 - precision: 0.8462 - val_loss: 0.3792 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 79/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3465 - accuracy: 0.8703 - recall: 0.2419 - precision: 0.8333 - val_loss: 0.3799 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 80/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3537 - accuracy: 0.8664 - recall: 0.2258 - precision: 0.8000 - val_loss: 0.3774 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 81/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3758 - accuracy: 0.8521 - recall: 0.1290 - precision: 0.7273 - val_loss: 0.3794 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 82/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3665 - accuracy: 0.8573 - recall: 0.1694 - precision: 0.7500 - val_loss: 0.3809 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 83/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3677 - accuracy: 0.8560 - recall: 0.1532 - precision: 0.7600 - val_loss: 0.3816 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 84/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3768 - accuracy: 0.8547 - recall: 0.1371 - precision: 0.7727 - val_loss: 0.3792 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 85/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3646 - accuracy: 0.8625 - recall: 0.1855 - precision: 0.8214 - val_loss: 0.3806 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 86/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3880 - accuracy: 0.8508 - recall: 0.1371 - precision: 0.6800 - val_loss: 0.3818 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 87/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3679 - accuracy: 0.8573 - recall: 0.1452 - precision: 0.8182 - val_loss: 0.3827 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 88/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3591 - accuracy: 0.8664 - recall: 0.2097 - precision: 0.8387 - val_loss: 0.3794 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 89/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3605 - accuracy: 0.8638 - recall: 0.2016 - precision: 0.8065 - val_loss: 0.3819 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 90/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3709 - accuracy: 0.8547 - recall: 0.1532 - precision: 0.7308 - val_loss: 0.3823 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 91/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3785 - accuracy: 0.8534 - recall: 0.1210 - precision: 0.7895 - val_loss: 0.3860 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 92/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3462 - accuracy: 0.8625 - recall: 0.1855 - precision: 0.8214 - val_loss: 0.3837 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 93/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3717 - accuracy: 0.8586 - recall: 0.1694 - precision: 0.7778 - val_loss: 0.3878 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 94/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3585 - accuracy: 0.8534 - recall: 0.1371 - precision: 0.7391 - val_loss: 0.3841 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 95/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3520 - accuracy: 0.8651 - recall: 0.1935 - precision: 0.8571 - val_loss: 0.3830 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 96/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3487 - accuracy: 0.8703 - recall: 0.2581 - precision: 0.8000 - val_loss: 0.3829 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 97/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3449 - accuracy: 0.8729 - recall: 0.2419 - precision: 0.8824 - val_loss: 0.3833 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 98/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3646 - accuracy: 0.8495 - recall: 0.1210 - precision: 0.6818 - val_loss: 0.3835 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 99/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3510 - accuracy: 0.8664 - recall: 0.2177 - precision: 0.8182 - val_loss: 0.3871 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 100/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3433 - accuracy: 0.8651 - recall: 0.2177 - precision: 0.7941 - val_loss: 0.3896 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 101/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3335 - accuracy: 0.8729 - recall: 0.2419 - precision: 0.8824 - val_loss: 0.3917 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 102/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3405 - accuracy: 0.8651 - recall: 0.2258 - precision: 0.7778 - val_loss: 0.3924 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 103/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3465 - accuracy: 0.8690 - recall: 0.2661 - precision: 0.7674 - val_loss: 0.3983 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 104/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3384 - accuracy: 0.8651 - recall: 0.1694 - precision: 0.9545 - val_loss: 0.3981 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 105/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3527 - accuracy: 0.8664 - recall: 0.2258 - precision: 0.8000 - val_loss: 0.3966 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 106/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3452 - accuracy: 0.8638 - recall: 0.1935 - precision: 0.8276 - val_loss: 0.3975 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 107/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3595 - accuracy: 0.8612 - recall: 0.2016 - precision: 0.7576 - val_loss: 0.3954 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 108/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3328 - accuracy: 0.8690 - recall: 0.2177 - precision: 0.8710 - val_loss: 0.3970 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 109/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3401 - accuracy: 0.8638 - recall: 0.2097 - precision: 0.7879 - val_loss: 0.3985 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 110/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3551 - accuracy: 0.8521 - recall: 0.1371 - precision: 0.7083 - val_loss: 0.4004 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 111/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3634 - accuracy: 0.8508 - recall: 0.1371 - precision: 0.6800 - val_loss: 0.4027 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 112/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3423 - accuracy: 0.8690 - recall: 0.2177 - precision: 0.8710 - val_loss: 0.4051 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 113/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3362 - accuracy: 0.8677 - recall: 0.2097 - precision: 0.8667 - val_loss: 0.4027 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 114/200 39/39 [==============================] - 0s 6ms/step - loss: 0.3517 - accuracy: 0.8547 - recall: 0.1774 - precision: 0.6875 - val_loss: 0.4019 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 115/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3578 - accuracy: 0.8482 - recall: 0.1210 - precision: 0.6522 - val_loss: 0.4091 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 116/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3454 - accuracy: 0.8560 - recall: 0.1532 - precision: 0.7600 - val_loss: 0.4119 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 117/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3566 - accuracy: 0.8547 - recall: 0.1371 - precision: 0.7727 - val_loss: 0.4137 - val_accuracy: 0.8369 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 Epoch 118/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3433 - accuracy: 0.8612 - recall: 0.1452 - precision: 0.9474 - val_loss: 0.4096 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 119/200 39/39 [==============================] - 0s 4ms/step - loss: 0.3295 - accuracy: 0.8677 - recall: 0.1935 - precision: 0.9231 - val_loss: 0.4042 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 120/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3476 - accuracy: 0.8534 - recall: 0.1452 - precision: 0.7200 - val_loss: 0.4075 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 121/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3312 - accuracy: 0.8664 - recall: 0.2419 - precision: 0.7692 - val_loss: 0.4084 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 122/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3502 - accuracy: 0.8586 - recall: 0.1694 - precision: 0.7778 - val_loss: 0.4142 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 123/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3274 - accuracy: 0.8664 - recall: 0.1935 - precision: 0.8889 - val_loss: 0.4172 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 124/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3334 - accuracy: 0.8612 - recall: 0.1774 - precision: 0.8148 - val_loss: 0.4141 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 125/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3184 - accuracy: 0.8742 - recall: 0.2419 - precision: 0.9091 - val_loss: 0.4135 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 126/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3212 - accuracy: 0.8703 - recall: 0.2581 - precision: 0.8000 - val_loss: 0.4122 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 127/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3188 - accuracy: 0.8703 - recall: 0.2823 - precision: 0.7609 - val_loss: 0.4163 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 128/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3223 - accuracy: 0.8651 - recall: 0.2500 - precision: 0.7381 - val_loss: 0.4165 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 129/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3336 - accuracy: 0.8573 - recall: 0.2016 - precision: 0.6944 - val_loss: 0.4162 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 130/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3129 - accuracy: 0.8703 - recall: 0.2581 - precision: 0.8000 - val_loss: 0.4226 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 131/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3237 - accuracy: 0.8677 - recall: 0.2258 - precision: 0.8235 - val_loss: 0.4217 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 132/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3480 - accuracy: 0.8508 - recall: 0.1532 - precision: 0.6552 - val_loss: 0.4220 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 133/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3375 - accuracy: 0.8651 - recall: 0.2097 - precision: 0.8125 - val_loss: 0.4237 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 134/200 39/39 [==============================] - 0s 4ms/step - loss: 0.3297 - accuracy: 0.8586 - recall: 0.2177 - precision: 0.6923 - val_loss: 0.4296 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 135/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3267 - accuracy: 0.8625 - recall: 0.1935 - precision: 0.8000 - val_loss: 0.4300 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 136/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3313 - accuracy: 0.8638 - recall: 0.1935 - precision: 0.8276 - val_loss: 0.4323 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 137/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3338 - accuracy: 0.8586 - recall: 0.2177 - precision: 0.6923 - val_loss: 0.4383 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 138/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3330 - accuracy: 0.8625 - recall: 0.1935 - precision: 0.8000 - val_loss: 0.4430 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 139/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3210 - accuracy: 0.8638 - recall: 0.1855 - precision: 0.8519 - val_loss: 0.4434 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 140/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3239 - accuracy: 0.8794 - recall: 0.2823 - precision: 0.8974 - val_loss: 0.4374 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 141/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3205 - accuracy: 0.8664 - recall: 0.2339 - precision: 0.7838 - val_loss: 0.4379 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 142/200 39/39 [==============================] - 0s 4ms/step - loss: 0.3159 - accuracy: 0.8677 - recall: 0.2258 - precision: 0.8235 - val_loss: 0.4360 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 143/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3269 - accuracy: 0.8586 - recall: 0.2177 - precision: 0.6923 - val_loss: 0.4388 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 144/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3175 - accuracy: 0.8664 - recall: 0.2500 - precision: 0.7561 - val_loss: 0.4452 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 145/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3147 - accuracy: 0.8742 - recall: 0.2823 - precision: 0.8140 - val_loss: 0.4441 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 146/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3114 - accuracy: 0.8703 - recall: 0.2742 - precision: 0.7727 - val_loss: 0.4483 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 147/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3372 - accuracy: 0.8651 - recall: 0.2339 - precision: 0.7632 - val_loss: 0.4505 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 148/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3239 - accuracy: 0.8729 - recall: 0.2661 - precision: 0.8250 - val_loss: 0.4476 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 149/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3114 - accuracy: 0.8742 - recall: 0.2823 - precision: 0.8140 - val_loss: 0.4469 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 150/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3414 - accuracy: 0.8625 - recall: 0.2500 - precision: 0.7045 - val_loss: 0.4513 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 151/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3101 - accuracy: 0.8768 - recall: 0.2661 - precision: 0.8919 - val_loss: 0.4552 - val_accuracy: 0.8399 - val_recall: 0.0185 - val_precision: 1.0000 Epoch 152/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3015 - accuracy: 0.8820 - recall: 0.3145 - precision: 0.8667 - val_loss: 0.4613 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 153/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3156 - accuracy: 0.8716 - recall: 0.2661 - precision: 0.8049 - val_loss: 0.4634 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 154/200 39/39 [==============================] - 0s 6ms/step - loss: 0.2951 - accuracy: 0.8833 - recall: 0.3306 - precision: 0.8542 - val_loss: 0.4636 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 155/200 39/39 [==============================] - 0s 6ms/step - loss: 0.2962 - accuracy: 0.8768 - recall: 0.3065 - precision: 0.8085 - val_loss: 0.4640 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 156/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3220 - accuracy: 0.8651 - recall: 0.2339 - precision: 0.7632 - val_loss: 0.4640 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 157/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3086 - accuracy: 0.8742 - recall: 0.3226 - precision: 0.7547 - val_loss: 0.4612 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 158/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3269 - accuracy: 0.8638 - recall: 0.2419 - precision: 0.7317 - val_loss: 0.4602 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 159/200 39/39 [==============================] - 0s 6ms/step - loss: 0.3181 - accuracy: 0.8690 - recall: 0.2661 - precision: 0.7674 - val_loss: 0.4667 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 160/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3121 - accuracy: 0.8755 - recall: 0.2823 - precision: 0.8333 - val_loss: 0.4711 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 161/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3154 - accuracy: 0.8690 - recall: 0.2661 - precision: 0.7674 - val_loss: 0.4733 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 162/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3265 - accuracy: 0.8599 - recall: 0.2016 - precision: 0.7353 - val_loss: 0.4753 - val_accuracy: 0.8429 - val_recall: 0.0370 - val_precision: 1.0000 Epoch 163/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3121 - accuracy: 0.8703 - recall: 0.2581 - precision: 0.8000 - val_loss: 0.4791 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 164/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3046 - accuracy: 0.8742 - recall: 0.2823 - precision: 0.8140 - val_loss: 0.4811 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 165/200 39/39 [==============================] - 0s 3ms/step - loss: 0.2920 - accuracy: 0.8820 - recall: 0.3306 - precision: 0.8367 - val_loss: 0.4778 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 166/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3201 - accuracy: 0.8664 - recall: 0.2177 - precision: 0.8182 - val_loss: 0.4785 - val_accuracy: 0.8550 - val_recall: 0.1111 - val_precision: 1.0000 Epoch 167/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3040 - accuracy: 0.8794 - recall: 0.3548 - precision: 0.7719 - val_loss: 0.4817 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000 Epoch 168/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3206 - accuracy: 0.8651 - recall: 0.2500 - precision: 0.7381 - val_loss: 0.4809 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 169/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3282 - accuracy: 0.8612 - recall: 0.2339 - precision: 0.7073 - val_loss: 0.4832 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 170/200 39/39 [==============================] - 0s 5ms/step - loss: 0.3162 - accuracy: 0.8664 - recall: 0.2177 - precision: 0.8182 - val_loss: 0.4828 - val_accuracy: 0.8459 - val_recall: 0.0556 - val_precision: 1.0000 Epoch 171/200 39/39 [==============================] - 0s 6ms/step - loss: 0.3176 - accuracy: 0.8612 - recall: 0.2177 - precision: 0.7297 - val_loss: 0.4865 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 172/200 39/39 [==============================] - 0s 6ms/step - loss: 0.3090 - accuracy: 0.8690 - recall: 0.2661 - precision: 0.7674 - val_loss: 0.4876 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000 Epoch 173/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3183 - accuracy: 0.8625 - recall: 0.2500 - precision: 0.7045 - val_loss: 0.4984 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 174/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3024 - accuracy: 0.8755 - recall: 0.2742 - precision: 0.8500 - val_loss: 0.5042 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 175/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3090 - accuracy: 0.8755 - recall: 0.2661 - precision: 0.8684 - val_loss: 0.5064 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000 Epoch 176/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3112 - accuracy: 0.8625 - recall: 0.2097 - precision: 0.7647 - val_loss: 0.5059 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 177/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3163 - accuracy: 0.8729 - recall: 0.2500 - precision: 0.8611 - val_loss: 0.5090 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000 Epoch 178/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3091 - accuracy: 0.8716 - recall: 0.2339 - precision: 0.8788 - val_loss: 0.5161 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 179/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3009 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5185 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000 Epoch 180/200 39/39 [==============================] - 0s 3ms/step - loss: 0.2993 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5108 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000 Epoch 181/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3158 - accuracy: 0.8703 - recall: 0.2823 - precision: 0.7609 - val_loss: 0.5172 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000 Epoch 182/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3082 - accuracy: 0.8755 - recall: 0.2742 - precision: 0.8500 - val_loss: 0.5207 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 183/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3106 - accuracy: 0.8690 - recall: 0.2581 - precision: 0.7805 - val_loss: 0.5184 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 184/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3051 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5218 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 185/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3113 - accuracy: 0.8690 - recall: 0.2419 - precision: 0.8108 - val_loss: 0.5248 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 186/200 39/39 [==============================] - 0s 2ms/step - loss: 0.3175 - accuracy: 0.8651 - recall: 0.2339 - precision: 0.7632 - val_loss: 0.5180 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 187/200 39/39 [==============================] - 0s 2ms/step - loss: 0.2984 - accuracy: 0.8690 - recall: 0.2419 - precision: 0.8108 - val_loss: 0.5206 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 188/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3052 - accuracy: 0.8690 - recall: 0.2984 - precision: 0.7255 - val_loss: 0.5214 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000 Epoch 189/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3000 - accuracy: 0.8833 - recall: 0.3306 - precision: 0.8542 - val_loss: 0.5295 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 190/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3024 - accuracy: 0.8677 - recall: 0.2500 - precision: 0.7750 - val_loss: 0.5297 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 191/200 39/39 [==============================] - 0s 3ms/step - loss: 0.2897 - accuracy: 0.8833 - recall: 0.3468 - precision: 0.8269 - val_loss: 0.5285 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 192/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3104 - accuracy: 0.8625 - recall: 0.2419 - precision: 0.7143 - val_loss: 0.5329 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 193/200 39/39 [==============================] - 0s 3ms/step - loss: 0.2969 - accuracy: 0.8716 - recall: 0.2742 - precision: 0.7907 - val_loss: 0.5362 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 194/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3191 - accuracy: 0.8547 - recall: 0.1935 - precision: 0.6667 - val_loss: 0.5417 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 195/200 39/39 [==============================] - 0s 3ms/step - loss: 0.2948 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5458 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 196/200 39/39 [==============================] - 0s 3ms/step - loss: 0.2989 - accuracy: 0.8703 - recall: 0.2500 - precision: 0.8158 - val_loss: 0.5518 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 197/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3147 - accuracy: 0.8703 - recall: 0.2823 - precision: 0.7609 - val_loss: 0.5514 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 198/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3016 - accuracy: 0.8755 - recall: 0.2903 - precision: 0.8182 - val_loss: 0.5512 - val_accuracy: 0.8520 - val_recall: 0.0926 - val_precision: 1.0000 Epoch 199/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3047 - accuracy: 0.8794 - recall: 0.3306 - precision: 0.8039 - val_loss: 0.5541 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000 Epoch 200/200 39/39 [==============================] - 0s 3ms/step - loss: 0.3025 - accuracy: 0.8755 - recall: 0.2500 - precision: 0.9118 - val_loss: 0.5626 - val_accuracy: 0.8489 - val_recall: 0.0741 - val_precision: 1.0000
ypredtest = nn.predict(xtest_scaled)
12/12 [==============================] - 0s 1ms/step
ypredtest
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[2.26473090e-10]], dtype=float32)
ypredtest = list(map(int,(ypredtest>0.5)))
print(ypredtest)
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
Accuracy = accuracy_score(ytest,ypredtest)
Accuracy
0.8478260869565217
model.history
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'val_precision': [0.16233766078948975,
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model.history.keys()
dict_keys(['loss', 'accuracy', 'recall', 'precision', 'val_loss', 'val_accuracy', 'val_recall', 'val_precision'])
# Visualizing accuracy
plt.plot(model.history['accuracy'])
plt.plot(model.history['val_accuracy'])
plt.legend(['training_accuracy','testing_accuracy'])
<matplotlib.legend.Legend at 0x2ea8d930b80>
# Visualizing loss
plt.plot(model.history['loss'])
plt.plot(model.history['val_loss'])
plt.legend(['training_loss','testing_loss'])
<matplotlib.legend.Legend at 0x2ea8d9f9190>
Predictions = log_model.predict(xtest)
Predictions
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pd.options.display.max_rows=368
final_output = pd.DataFrame(Predictions,columns=['Final_Predictions'])
final_output
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from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.cluster import AgglomerativeClustering
from sklearn.preprocessing import StandardScaler, normalize
from sklearn.metrics import silhouette_score
import scipy.cluster.hierarchy as shc
x
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | 1.153254 | -0.108350 | 0.726020 | 2.125136 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.421642 | -2.171982 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | 0.245834 |
| 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.660853 | -0.291719 | 1.488876 | -0.678049 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | -0.164511 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 0.806541 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | 0.246200 | -0.937654 | -1.674841 | 1.324226 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.550208 | 0.155707 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 |
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | 0.246200 | -0.763634 | 1.243211 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 0.252146 | -1.155935 |
| 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.660853 | -0.644858 | 0.325900 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.678774 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | 1.153254 | -0.835451 | -0.284329 | 0.523316 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.314873 |
| 1466 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | -1.567907 | 0.741140 | 1.004010 | 0.523316 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.293077 | 1.707500 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | 0.806541 |
| 1467 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.660853 | -0.076690 | -1.284418 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -0.678774 | -2.171982 | 0.338096 | -0.164613 | -0.615492 | -0.679146 | -0.314873 |
| 1468 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.660853 | -0.236474 | -0.150393 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.735447 | 0.155707 | -1.077862 | 0.325228 | 0.488900 | -0.679146 | 1.086895 |
| 1469 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | 0.246200 | -0.445978 | -0.574124 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 1.754054 | -0.491174 | -0.339394 | -0.368715 | -0.595227 |
1470 rows × 58 columns
pca = PCA(n_components = 2)
X_principal = pca.fit_transform(x)
X_principal = pd.DataFrame(X_principal)
X_principal.columns = ['P1', 'P2']
X_principal
| P1 | P2 | |
|---|---|---|
| 0 | -0.123227 | 1.753045 |
| 1 | 0.708353 | -1.417164 |
| 2 | -2.489738 | 1.244741 |
| 3 | -0.952054 | -0.341545 |
| 4 | -1.946408 | 0.797334 |
| ... | ... | ... |
| 1465 | -0.837692 | 0.375050 |
| 1466 | 0.762103 | -0.371828 |
| 1467 | -1.293807 | -0.939619 |
| 1468 | 1.156983 | 0.414920 |
| 1469 | -1.291327 | 0.138558 |
1470 rows × 2 columns
kmeans_model = KMeans(n_clusters=3) # n_clusters=8 >> default
kmeans_model.fit(X_principal)
KMeans(n_clusters=3)
kmeans_model.inertia_
3686.284119036042
y_var = kmeans_model.fit_predict(X_principal)
y_var
array([1, 2, 1, ..., 1, 2, 1])
index_0 = np.where(y_var==0)
index_0
(array([ 18, 25, 28, 29, 45, 62, 63, 65, 85, 90, 93,
95, 98, 105, 106, 110, 112, 119, 123, 126, 147, 158,
165, 178, 186, 187, 189, 190, 194, 213, 218, 231, 233,
235, 237, 244, 257, 263, 268, 270, 271, 279, 280, 290,
295, 300, 311, 314, 316, 326, 329, 367, 375, 379, 390,
392, 400, 401, 406, 408, 411, 417, 424, 425, 427, 429,
445, 448, 465, 466, 473, 477, 489, 492, 497, 502, 510,
533, 534, 535, 538, 544, 552, 561, 568, 584, 588, 592,
595, 609, 616, 624, 625, 627, 646, 649, 653, 674, 677,
695, 699, 701, 714, 716, 721, 723, 728, 736, 738, 741,
743, 745, 746, 749, 750, 755, 758, 760, 766, 770, 771,
774, 787, 789, 799, 804, 806, 810, 812, 813, 814, 821,
837, 838, 851, 858, 861, 867, 869, 890, 894, 898, 899,
904, 907, 913, 914, 916, 918, 919, 922, 926, 936, 937,
945, 947, 954, 955, 956, 962, 966, 971, 975, 976, 994,
999, 1008, 1009, 1010, 1024, 1031, 1034, 1043, 1054, 1055, 1076,
1078, 1080, 1086, 1093, 1096, 1103, 1111, 1116, 1126, 1129, 1135,
1138, 1140, 1154, 1156, 1162, 1166, 1176, 1177, 1181, 1184, 1185,
1194, 1195, 1221, 1223, 1225, 1242, 1264, 1268, 1275, 1277, 1278,
1295, 1301, 1303, 1310, 1327, 1330, 1331, 1348, 1351, 1357, 1374,
1377, 1401, 1403, 1414, 1430, 1437, 1443, 1445], dtype=int64),)
cluster_0 = x.iloc[index_0]
cluster_0
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 18 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 1.032581 | -0.887515 | 1.061787 | 0.0 | -1.664727 | -1.575686 | 0.595834 | -1.026167 | 1.749610 | 1.153254 | 1.896174 | 1.083275 | -0.277594 | 0.216054 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.535365 | 0.155707 | 0.338096 | 2.937711 | 1.041095 | 0.252146 | 0.806541 |
| 25 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 1.188765 | -0.517332 | 0.085049 | 0.0 | -1.649772 | 0.254625 | -0.388296 | 0.379672 | 2.653309 | 0.246200 | 2.675333 | -0.502870 | 0.523316 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.892537 | 0.155707 | -1.077862 | 1.141629 | 2.421585 | 0.562576 | 1.086895 |
| 28 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | -0.806912 | -0.270544 | 1.061787 | 0.0 | -1.643126 | -1.575686 | -1.175601 | -1.026167 | 0.845911 | 1.153254 | 0.795747 | -1.717284 | 0.122861 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.635406 | 0.931603 | 0.338096 | 2.447870 | 0.488900 | 0.873006 | 3.610079 |
| 29 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.241677 | -0.887515 | 1.061787 | 0.0 | -1.639803 | -0.660531 | 0.841867 | 0.379672 | 2.653309 | -1.567907 | 2.644099 | 1.195848 | 0.122861 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.378275 | -0.620189 | -1.077862 | -0.817734 | -0.615492 | -0.058285 | -0.875581 |
| 45 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | 1.382135 | 0.346427 | 0.085049 | 0.0 | -1.606570 | -0.660531 | -0.831155 | 0.379672 | 2.653309 | 0.246200 | 2.771161 | 0.276429 | -0.678049 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.506840 | -2.171982 | 0.338096 | 2.447870 | 2.973780 | 3.977310 | 1.086895 |
| 62 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 0.462388 | -0.270544 | -0.891688 | 0.0 | -1.570014 | -0.660531 | -1.126394 | -1.026167 | 2.653309 | 0.246200 | 2.600116 | 0.335597 | 0.923771 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.278234 | -0.620189 | -1.077862 | 3.264271 | -0.339394 | 3.356449 | 1.086895 |
| 63 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.417384 | 1.568068 | 1.950552 | 0.085049 | 0.0 | -1.568353 | -1.575686 | 1.629171 | 0.379672 | 0.845911 | -1.567907 | 0.240965 | -1.680744 | 1.724681 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.149668 | 0.155707 | -1.077862 | 2.284590 | 3.249878 | 1.493867 | 1.367249 |
| 65 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | 0.083085 | -0.147150 | 0.085049 | 0.0 | -1.563368 | 1.169781 | -1.618459 | 0.379672 | 1.749610 | 0.246200 | 1.753601 | 0.761296 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 2.589994 | 1.249709 | -0.620189 | 0.338096 | -0.327893 | -1.167687 | -0.679146 | -0.595227 |
| 85 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | 1.481299 | -0.270544 | 0.085049 | 0.0 | -1.516842 | 1.169781 | -0.831155 | -2.432006 | 0.845911 | 1.153254 | 0.160861 | 1.037880 | 0.523316 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 3.306758 | 0.155707 | -1.077862 | -0.164613 | -0.063296 | -0.679146 | -0.595227 |
| 90 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | -0.675520 | -1.010909 | 1.061787 | 0.0 | -1.505211 | 0.254625 | 0.595834 | -1.026167 | 1.749610 | -0.660853 | 1.487365 | -0.027842 | -0.678049 | 1.855984 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 1.378275 | 0.155707 | -1.077862 | 2.447870 | -0.339394 | 2.735589 | 1.927956 |
| 93 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.993860 | -0.407777 | -1.010909 | 0.085049 | 0.0 | -1.496903 | 0.254625 | -1.274014 | -1.026167 | 0.845911 | -1.567907 | 0.886051 | -1.569997 | -0.277594 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 1.707500 | -1.077862 | 0.488508 | 1.317193 | 2.114728 | 0.245834 |
| 95 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 1.027623 | -0.887515 | 1.061787 | 0.0 | -1.493579 | -1.575686 | -0.289883 | 0.379672 | 0.845911 | 0.246200 | 1.497139 | 1.361546 | 2.525591 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 0.606882 | 1.707500 | -2.493820 | -0.491174 | -0.339394 | -0.679146 | -0.314873 |
| 98 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.307882 | -0.298696 | 0.099639 | 1.061787 | 0.0 | -1.485271 | 1.169781 | -1.421633 | 0.379672 | 1.749610 | 0.246200 | 1.565770 | 1.418887 | -1.078504 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 3.435323 | -1.396086 | -1.077862 | 4.897073 | 1.593291 | -0.368715 | 1.086895 |
| 105 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.417384 | 1.530881 | -0.887515 | 1.061787 | 0.0 | -1.470317 | 0.254625 | -1.667666 | -1.026167 | 2.653309 | 1.153254 | 2.622214 | 1.069361 | 2.525591 | 1.582663 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 2.406799 | 0.155707 | 0.338096 | -0.654454 | -0.615492 | -0.058285 | -0.595227 |
| 106 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 0.774755 | -1.010909 | 0.085049 | 0.0 | -1.468655 | -1.575686 | 0.349801 | 0.379672 | 2.653309 | -0.660853 | 2.479428 | -0.640600 | 0.122861 | 1.036019 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 2.149668 | -1.396086 | -1.077862 | 0.161947 | -0.339394 | -0.679146 | 0.806541 |
| 110 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 1.620129 | -1.010909 | 1.061787 | 0.0 | -1.462009 | -1.575686 | -1.766079 | -1.026167 | 0.845911 | -1.567907 | 0.208456 | 1.613817 | 0.122861 | 1.309341 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 1.506840 | -1.396086 | -1.077862 | 0.978348 | 2.145487 | 3.046019 | 1.086895 |
| 112 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.869874 | -1.637412 | 2.073946 | 0.085049 | 0.0 | -1.457024 | 1.169781 | -1.766079 | 1.785511 | 1.749610 | 1.153254 | 2.300096 | -0.062134 | -0.277594 | -0.877232 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.506840 | 0.155707 | 0.338096 | -0.327893 | -0.339394 | 0.562576 | -0.034520 |
| 119 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.665354 | -1.012678 | 2.073946 | -0.891688 | 0.0 | -1.440407 | 0.254625 | 1.284725 | 0.379672 | 1.749610 | 1.153254 | 2.221691 | 0.728128 | -0.678049 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 1.763972 | 0.155707 | 1.754054 | 2.937711 | 2.145487 | 0.562576 | 2.208310 |
| 123 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | -0.293738 | -0.393938 | 0.085049 | 0.0 | -1.433761 | -1.575686 | -0.732742 | 0.379672 | 2.653309 | 0.246200 | 2.769461 | -1.103401 | 1.724681 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.506840 | 1.707500 | 0.338096 | 2.121310 | 3.802074 | 3.977310 | 3.049371 |
| 126 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.307882 | -1.625016 | 1.703764 | 1.061787 | 0.0 | -1.428776 | 1.169781 | 1.383138 | 0.379672 | 0.845911 | 1.153254 | 0.809346 | -1.524603 | -0.678049 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 3.692454 | 0.155707 | -1.077862 | 5.386914 | 1.593291 | 3.977310 | 0.526188 |
| 147 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | 0.135146 | 0.099639 | 0.085049 | 0.0 | -1.372281 | 1.169781 | 1.235519 | -1.026167 | 1.749610 | -1.567907 | 2.268862 | -0.200285 | 0.523316 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.249709 | -0.620189 | -1.077862 | -0.001333 | 0.488900 | 1.493867 | 0.806541 |
| 158 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | -0.427610 | -0.640727 | 1.061787 | 0.0 | -1.345695 | 0.254625 | 0.054562 | -1.026167 | 0.845911 | 1.153254 | 0.924722 | -0.809671 | 1.724681 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 0.478316 | -0.620189 | -1.077862 | 0.815068 | 1.869389 | -0.058285 | 1.927956 |
| 165 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 1.610212 | 0.223033 | 0.085049 | 0.0 | -1.327417 | 0.254625 | -0.634329 | 0.379672 | 2.653309 | -0.660853 | 2.852116 | 0.385068 | 0.122861 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.249709 | 1.707500 | 0.338096 | -0.327893 | -0.063296 | 0.562576 | -0.034520 |
| 178 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.685436 | -1.010909 | -0.891688 | 0.0 | -1.297507 | -0.660531 | 1.284725 | 0.379672 | 0.845911 | -1.567907 | 0.839305 | -1.711241 | -0.678049 | 2.675949 | 2.346151 | 0.266233 | 0.0 | 2.589994 | 1.635406 | -0.620189 | 0.338096 | 2.774431 | 2.421585 | 3.977310 | 0.806541 |
| 186 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 0.462388 | -0.640727 | -1.868426 | 0.0 | -1.282553 | 1.169781 | -0.978775 | 0.379672 | 2.653309 | 0.246200 | 2.662372 | -1.098201 | -0.678049 | -0.330589 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.249709 | -0.620189 | 0.338096 | 2.121310 | 1.041095 | 2.114728 | 1.367249 |
| 187 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -0.273905 | 0.593216 | 1.061787 | 0.0 | -1.280891 | 0.254625 | -0.240677 | 1.785511 | 2.653309 | -0.660853 | 2.596291 | -0.136901 | 2.125136 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 3.178192 | 0.155707 | 0.338096 | 2.774431 | 2.973780 | -0.058285 | 3.049371 |
| 189 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | -1.213485 | -0.764121 | 0.085049 | 0.0 | -1.274245 | 1.169781 | 1.579964 | 0.379672 | 1.749610 | -0.660853 | 1.536448 | -1.000807 | 0.122861 | 0.762698 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.249709 | 2.483396 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | -1.155935 |
| 190 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | -0.256552 | -1.010909 | 1.061787 | 0.0 | -1.272583 | 0.254625 | -0.043851 | -1.026167 | 2.653309 | 0.246200 | 2.867626 | -1.213586 | -1.078504 | -0.330589 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.921061 | 1.707500 | 0.338096 | 4.243953 | 3.802074 | 2.735589 | 1.367249 |
| 194 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 0.973083 | -0.887515 | -0.891688 | 0.0 | -1.264275 | -1.575686 | -0.043851 | -1.026167 | 1.749610 | 1.153254 | 2.186207 | 0.864172 | 2.525591 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 1.378275 | -1.396086 | 0.338096 | 2.121310 | 1.041095 | 2.735589 | 1.086895 |
| 213 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 1.652357 | -0.147150 | 1.061787 | 0.0 | -1.211103 | -0.660531 | 0.743454 | -1.026167 | 0.845911 | -0.660853 | 1.272124 | 0.199975 | 0.923771 | 0.216054 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 0.606882 | 1.707500 | -2.493820 | 0.488508 | 1.317193 | 0.562576 | 0.806541 |
| 218 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 0.618571 | -0.393938 | 0.085049 | 0.0 | -1.201133 | 1.169781 | -0.437503 | -1.026167 | 0.845911 | 1.153254 | 0.501889 | 0.355132 | 1.324226 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.506840 | -0.620189 | 0.338096 | 1.958030 | 0.764998 | 3.046019 | 1.086895 |
| 231 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -0.670562 | -0.640727 | -0.891688 | 0.0 | -1.172885 | 0.254625 | -0.388296 | 0.379672 | 2.653309 | 1.153254 | 2.704655 | -1.318289 | -0.678049 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.378275 | 0.155707 | 0.338096 | 2.447870 | 3.525976 | 2.735589 | 3.049371 |
| 233 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.127709 | -1.010909 | 1.061787 | 0.0 | -1.166239 | 1.169781 | 0.103769 | 0.379672 | 2.653309 | 1.153254 | 2.765212 | 1.377989 | 0.122861 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | -1.077862 | -0.001333 | -1.167687 | -0.679146 | 0.526188 |
| 235 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.665354 | 0.573948 | 0.840004 | 0.085049 | 0.0 | -1.159592 | 1.169781 | 0.694247 | 0.379672 | 1.749610 | 1.153254 | 2.031523 | -0.923228 | 0.923771 | 1.855984 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | 1.631469 | 2.421585 | -0.368715 | 1.367249 |
| 237 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.650871 | -0.078056 | -0.887515 | 1.061787 | 0.0 | -1.156269 | -1.575686 | 0.645041 | -1.026167 | 2.653309 | 0.246200 | 2.669809 | 0.943999 | -0.678049 | 0.762698 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.792496 | -0.620189 | 1.754054 | 4.243953 | 0.764998 | 3.977310 | 2.208310 |
| 244 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | -1.364710 | -1.010909 | 0.085049 | 0.0 | -1.144638 | 0.254625 | 0.202182 | 1.785511 | 2.653309 | 1.153254 | 2.698281 | 0.232862 | -1.078504 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.763972 | -0.620189 | 0.338096 | 2.774431 | -1.167687 | -0.368715 | 0.806541 |
| 257 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 1.520965 | -0.887515 | -0.891688 | 0.0 | -1.118052 | -1.575686 | -0.831155 | 0.379672 | 2.653309 | 0.246200 | 2.748001 | -1.175499 | -1.078504 | 1.036019 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.378275 | 1.707500 | 0.338096 | 2.284590 | 0.764998 | 0.252146 | 1.367249 |
| 263 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.993860 | -0.779642 | -0.887515 | 0.085049 | 0.0 | -1.099774 | 0.254625 | 0.448214 | -2.432006 | 1.749610 | -0.660853 | 2.203206 | 0.093305 | 0.122861 | -0.877232 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.149668 | -0.620189 | -1.077862 | -0.001333 | 0.764998 | 1.493867 | 0.806541 |
| 268 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.884358 | 1.444112 | 1.333581 | -0.891688 | 0.0 | -1.084819 | 0.254625 | 0.645041 | 0.379672 | 1.749610 | 1.153254 | 1.485878 | -0.957379 | -1.078504 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 2.121310 | 0.764998 | 0.562576 | 1.647603 |
| 270 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -0.868890 | -1.010909 | 0.085049 | 0.0 | -1.081496 | 1.169781 | 0.743454 | 0.379672 | 2.653309 | -1.567907 | 2.664922 | 0.649987 | -1.078504 | -0.330589 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 3.306758 | -0.620189 | 0.338096 | 4.733793 | 1.593291 | 0.562576 | 2.488664 |
| 271 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.103362 | -0.338362 | 2.444129 | 1.061787 | 0.0 | -1.078173 | -1.575686 | 1.087899 | 0.379672 | 0.845911 | -0.660853 | 1.135925 | -0.568503 | -0.678049 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.164511 | -0.620189 | -1.077862 | 0.488508 | 0.764998 | 2.114728 | 1.367249 |
| 279 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | -0.013600 | -0.640727 | -1.868426 | 0.0 | -1.063218 | -1.575686 | 1.481551 | 0.379672 | 2.653309 | -0.660853 | 2.685957 | 0.211078 | 0.122861 | -0.330589 | -0.426230 | -1.584178 | 0.0 | 1.415991 | 2.149668 | 0.931603 | -1.077862 | 0.488508 | -0.063296 | -0.368715 | 0.526188 |
| 280 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.760373 | 0.663195 | -0.764121 | 1.061787 | 0.0 | -1.061556 | 0.254625 | -1.027981 | 0.379672 | 1.749610 | 0.246200 | 2.354491 | 0.942031 | 0.122861 | 0.216054 | -0.426230 | 1.191438 | 0.0 | 2.589994 | 1.249709 | 1.707500 | -1.077862 | -0.327893 | -0.339394 | -0.368715 | -0.314873 |
| 290 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.322365 | -0.412735 | 0.099639 | 1.061787 | 0.0 | -1.044940 | 0.254625 | -1.520046 | 0.379672 | 2.653309 | -1.567907 | 2.584180 | 1.585428 | 2.525591 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.378275 | 0.931603 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.595227 |
| 295 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -0.613542 | 2.073946 | 0.085049 | 0.0 | -1.031647 | 0.254625 | 0.546627 | 0.379672 | 1.749610 | -0.660853 | 1.492040 | 0.077424 | 0.923771 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | -0.620189 | 1.754054 | 2.121310 | -0.063296 | 0.562576 | 1.086895 |
| 300 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -1.161424 | -0.887515 | 1.061787 | 0.0 | -1.021677 | 1.169781 | 1.087899 | 0.379672 | 1.749610 | -0.660853 | 2.021112 | 0.222462 | -0.678049 | 1.036019 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.378275 | -0.620189 | 0.338096 | 2.447870 | 1.593291 | -0.679146 | -0.034520 |
| 311 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 1.106955 | -0.270544 | 0.085049 | 0.0 | -0.996753 | -1.575686 | 1.530758 | 0.379672 | 0.845911 | -1.567907 | -0.274720 | 0.842528 | -0.678049 | 0.762698 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.635406 | -0.620189 | 0.338096 | 2.774431 | 1.317193 | 2.114728 | 1.927956 |
| 314 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0.227347 | -1.699389 | 0.099639 | -1.868426 | 0.0 | -0.990106 | 0.254625 | 1.629171 | 0.379672 | 1.749610 | -1.567907 | 2.244852 | -1.258980 | -0.678049 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.249709 | 0.155707 | 0.338096 | 2.284590 | 1.317193 | 2.735589 | 1.647603 |
| 316 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.322365 | 0.715256 | -1.010909 | -0.891688 | 0.0 | -0.986783 | 0.254625 | 1.186312 | -1.026167 | 1.749610 | 0.246200 | 1.585318 | 0.491457 | 1.724681 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.763972 | -0.620189 | 0.338096 | -0.001333 | -0.891589 | -0.679146 | 0.806541 |
| 326 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.323487 | -0.270544 | -0.891688 | 0.0 | -0.965182 | 0.254625 | -0.585122 | -1.026167 | 2.653309 | 1.153254 | 2.713155 | 0.959599 | -0.678049 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.249709 | -0.620189 | 0.338096 | 2.284590 | 1.317193 | 3.356449 | -0.314873 |
| 329 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 1.684585 | -0.517332 | 2.038524 | 0.0 | -0.960197 | 1.169781 | -1.175601 | 0.379672 | 2.653309 | 0.246200 | 2.506625 | 0.289781 | 0.523316 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.249709 | -0.620189 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.875581 |
| 367 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.603697 | 0.099639 | 0.085049 | 0.0 | -0.887085 | 1.169781 | 1.678377 | -1.026167 | 0.845911 | 1.153254 | 0.848442 | -1.624387 | 1.324226 | -0.057267 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.121144 | -0.620189 | 0.338096 | -0.491174 | -0.339394 | -0.368715 | -0.314873 |
| 375 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 1.136704 | -0.270544 | 0.085049 | 0.0 | -0.873792 | -0.660531 | -1.716872 | -1.026167 | 0.845911 | 0.246200 | 0.948094 | -0.315810 | 2.125136 | 2.402628 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 1.892537 | -0.620189 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -1.155935 |
| 379 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | 1.260659 | -0.887515 | 0.085049 | 0.0 | -0.863823 | 0.254625 | 1.530758 | 0.379672 | 1.749610 | 1.153254 | 2.157948 | 1.257124 | -0.277594 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.406799 | -0.620189 | 0.338096 | -0.327893 | -0.063296 | -0.368715 | -0.595227 |
| 390 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 0.445034 | 0.346427 | 0.085049 | 0.0 | -0.838898 | -1.575686 | -0.339090 | -1.026167 | 1.749610 | -0.660853 | 1.664360 | -1.400927 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.763972 | 0.155707 | 0.338096 | 2.937711 | 1.593291 | 0.252146 | 1.367249 |
| 392 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.045899 | -0.517332 | -0.891688 | 0.0 | -0.835575 | -1.575686 | 0.989486 | 0.379672 | 2.653309 | -1.567907 | 2.741627 | -0.815714 | 0.523316 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.635406 | 0.931603 | -1.077862 | -0.491174 | -0.615492 | -0.368715 | -0.595227 |
| 400 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0.227347 | 1.030102 | -1.010909 | -1.868426 | 0.0 | -0.820620 | -0.660531 | -0.683535 | 0.379672 | 2.653309 | 0.246200 | 2.697219 | -0.857314 | -0.678049 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 0.155707 | 0.338096 | 2.284590 | 1.041095 | -0.368715 | 0.526188 |
| 401 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | 0.256622 | -0.393938 | 0.085049 | 0.0 | -0.818959 | 0.254625 | 0.989486 | 1.785511 | 1.749610 | -1.567907 | 1.425534 | 0.554138 | 2.525591 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 2.589994 | 3.178192 | -2.171982 | -1.077862 | -0.001333 | 0.764998 | 1.493867 | 0.806541 |
| 406 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.650871 | -1.198611 | -0.764121 | 0.085049 | 0.0 | -0.800681 | 1.169781 | -1.323220 | -1.026167 | 0.845911 | 0.246200 | 0.311508 | 0.744290 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.149668 | 0.931603 | 0.338096 | -0.327893 | -0.063296 | -0.679146 | -0.034520 |
| 408 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | 1.704418 | -0.640727 | -0.891688 | 0.0 | -0.795696 | 1.169781 | -1.766079 | 0.379672 | 1.749610 | 1.153254 | 2.135850 | -0.562600 | -0.277594 | -0.603911 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.535365 | -0.620189 | -2.493820 | -0.327893 | -0.615492 | -0.368715 | -0.034520 |
| 411 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.526886 | -0.943263 | -0.270544 | 0.085049 | 0.0 | -0.790711 | -1.575686 | -1.224807 | 0.379672 | 2.653309 | -1.567907 | 2.775623 | -1.469932 | 0.923771 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.792496 | 1.707500 | -2.493820 | 3.590832 | 1.041095 | 2.735589 | 1.647603 |
| 417 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 1.476341 | -0.887515 | 1.061787 | 0.0 | -0.775756 | 0.254625 | 0.645041 | 0.379672 | 2.653309 | 0.246200 | 2.451593 | -0.181453 | -1.078504 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 2.121310 | 2.973780 | -0.368715 | 2.208310 |
| 424 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.198380 | -1.468833 | 2.444129 | 0.085049 | 0.0 | -0.759140 | -1.575686 | -0.486709 | -1.026167 | 1.749610 | 1.153254 | 1.618040 | 1.094659 | 0.122861 | -0.877232 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | -1.077862 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 425 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.735089 | 2.444129 | 1.061787 | 0.0 | -0.757478 | -0.660531 | 1.087899 | -1.026167 | 1.749610 | 0.246200 | 2.240177 | -0.702579 | -1.078504 | -0.057267 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.149668 | -0.620189 | 0.338096 | 3.264271 | 1.593291 | 3.977310 | 0.806541 |
| 427 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.526886 | 1.726730 | 2.320735 | 0.085049 | 0.0 | -0.750832 | 0.254625 | 0.694247 | -1.026167 | 0.845911 | -1.567907 | 0.799572 | -1.611738 | 0.523316 | 1.036019 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.378275 | 1.707500 | 1.754054 | 1.794749 | 2.421585 | 3.356449 | 1.927956 |
| 429 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 0.511970 | -0.887515 | 0.085049 | 0.0 | -0.747509 | -1.575686 | -0.732742 | 0.379672 | 1.749610 | 0.246200 | 2.413347 | -1.690019 | 1.324226 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.892537 | -0.620189 | -2.493820 | -0.654454 | -0.615492 | -0.679146 | -0.875581 |
| 445 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | 0.779713 | 1.086793 | 2.038524 | 0.0 | -0.710953 | -1.575686 | 0.841867 | 0.379672 | 1.749610 | -0.660853 | 2.195344 | -0.624016 | 0.122861 | 2.129306 | 2.346151 | 1.191438 | 0.0 | -0.932014 | 3.306758 | -0.620189 | 0.338096 | 0.488508 | 1.317193 | 1.493867 | 0.806541 |
| 448 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | -1.240755 | -0.393938 | 0.085049 | 0.0 | -0.704306 | -0.660531 | 0.448214 | 0.379672 | 1.749610 | 0.246200 | 1.430846 | 0.850399 | 1.724681 | -0.057267 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.378275 | 0.155707 | 0.338096 | 2.121310 | 0.488900 | 0.873006 | 2.488664 |
| 465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 0.573948 | 1.086793 | -1.868426 | 0.0 | -0.666089 | -1.575686 | 0.989486 | 0.379672 | 0.845911 | 0.246200 | 0.855029 | -0.748957 | 0.923771 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.149668 | 0.155707 | -1.077862 | -0.817734 | -0.615492 | -0.368715 | -0.595227 |
| 466 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | 1.173890 | -0.887515 | 2.038524 | 0.0 | -0.664427 | -0.660531 | 1.235519 | 0.379672 | 1.749610 | -1.567907 | 2.144349 | -1.220894 | 1.724681 | 0.216054 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.378275 | -0.620189 | 0.338096 | 1.794749 | 3.249878 | 2.735589 | 1.086895 |
| 473 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.322365 | 1.097038 | 1.086793 | 1.061787 | 0.0 | -0.642826 | 1.169781 | -0.388296 | -1.026167 | 2.653309 | 0.246200 | 2.762025 | -1.712927 | -0.678049 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.535365 | 1.707500 | 0.338096 | 3.917392 | 1.317193 | -0.679146 | 1.367249 |
| 477 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 1.099517 | -0.764121 | 0.085049 | 0.0 | -0.632856 | -1.575686 | 1.629171 | 0.379672 | 2.653309 | -0.660853 | 2.485377 | -0.887390 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.663930 | -0.620189 | 0.338096 | 4.080672 | 0.212802 | 2.425158 | 0.806541 |
| 489 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.665354 | -0.050786 | -0.393938 | 1.061787 | 0.0 | -0.604609 | -0.660531 | -0.781948 | -1.026167 | 1.749610 | 1.153254 | 2.151148 | -1.636192 | 0.523316 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 0.155707 | -1.077862 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 492 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | 1.074726 | -1.010909 | 1.061787 | 0.0 | -0.599624 | 1.169781 | -1.274014 | -1.026167 | 1.749610 | -1.567907 | 1.890862 | 0.517738 | 1.724681 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.249709 | 0.155707 | -2.493820 | -0.654454 | -0.615492 | -0.679146 | -0.595227 |
| 497 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.774856 | 1.270575 | -0.764121 | 1.061787 | 0.0 | -0.587992 | 1.169781 | -1.520046 | 0.379672 | 2.653309 | 1.153254 | 2.764362 | -0.696395 | 0.523316 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.892537 | -0.620189 | 1.754054 | -0.817734 | -0.615492 | -0.679146 | -0.875581 |
| 502 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | -1.399418 | -1.010909 | -1.868426 | 0.0 | -0.569714 | 1.169781 | -1.569253 | 0.379672 | -0.057788 | -1.567907 | 0.399050 | -0.956536 | 1.724681 | 1.309341 | 2.346151 | 1.191438 | 0.0 | -0.932014 | 0.864013 | -0.620189 | 1.754054 | 1.141629 | 0.764998 | 1.804297 | 1.647603 |
| 510 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | -1.005241 | 1.210187 | 1.061787 | 0.0 | -0.543128 | 0.254625 | 1.087899 | 0.379672 | 0.845911 | -0.660853 | 0.897099 | -1.065877 | -0.277594 | -0.057267 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.606882 | -1.396086 | 1.754054 | 0.325228 | 0.764998 | 1.493867 | -0.875581 |
| 533 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0.336849 | -0.551565 | -0.517332 | 1.061787 | 0.0 | -0.491618 | 1.169781 | -0.880361 | -1.026167 | 0.845911 | -1.567907 | 0.843980 | 1.329362 | 0.923771 | 1.582663 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 1.121144 | -0.620189 | 0.338096 | 1.794749 | 2.421585 | -0.368715 | 2.208310 |
| 534 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 0.415285 | -0.270544 | 0.085049 | 0.0 | -0.489956 | 0.254625 | -0.339090 | 1.785511 | 1.749610 | 0.246200 | 1.765925 | -0.112307 | 0.122861 | 1.036019 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.663930 | 0.155707 | 0.338096 | -0.327893 | -0.891589 | -0.368715 | -0.314873 |
| 535 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -0.930867 | 0.099639 | 1.061787 | 0.0 | -0.488295 | -0.660531 | 0.349801 | -1.026167 | 2.653309 | 1.153254 | 2.685320 | -0.766244 | 0.122861 | -0.057267 | -0.426230 | -0.658973 | 0.0 | 2.589994 | 1.506840 | -0.620189 | -1.077862 | 2.284590 | 0.488900 | 3.046019 | 0.526188 |
| 538 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -1.211006 | -1.010909 | 0.085049 | 0.0 | -0.483310 | 1.169781 | -0.339090 | -1.026167 | 2.653309 | 0.246200 | 2.695519 | 0.737685 | -0.678049 | -0.877232 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | 2.447870 | 0.764998 | -0.058285 | 1.647603 |
| 544 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.103362 | -1.451479 | -0.764121 | 0.085049 | 0.0 | -0.463370 | 1.169781 | -0.831155 | 0.379672 | 1.749610 | 0.246200 | 1.544097 | -0.574546 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 2.149668 | -0.620189 | -1.077862 | 2.447870 | -0.615492 | 2.735589 | 2.488664 |
| 552 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | 0.073169 | -0.023755 | 0.085049 | 0.0 | -0.436784 | 0.254625 | 0.743454 | 0.379672 | 1.749610 | 1.153254 | 0.977416 | 0.858269 | 1.724681 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.406799 | -1.396086 | -1.077862 | 0.488508 | 0.764998 | -0.368715 | -0.875581 |
| 561 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | -0.449922 | -0.764121 | 1.061787 | 0.0 | -0.413521 | 0.254625 | -1.716872 | -1.026167 | 1.749610 | -1.567907 | 2.199806 | -0.594362 | -0.678049 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 2.921061 | 0.155707 | 1.754054 | 4.407233 | 0.488900 | -0.368715 | 3.329725 |
| 568 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -0.192095 | -0.887515 | 0.085049 | 0.0 | -0.395244 | 1.169781 | 0.595834 | 0.379672 | 2.653309 | -1.567907 | 2.837879 | 0.967750 | 0.923771 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.635406 | -0.620189 | 0.338096 | -0.327893 | -0.615492 | -0.368715 | -0.034520 |
| 584 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -0.576356 | -0.147150 | 0.085049 | 0.0 | -0.358688 | -0.660531 | 0.005356 | 0.379672 | 2.653309 | 1.153254 | 2.534247 | 0.268700 | -0.678049 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.635406 | 0.931603 | -1.077862 | 2.774431 | 0.764998 | 3.666880 | 1.367249 |
| 588 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | -0.276384 | -0.887515 | 0.085049 | 0.0 | -0.348718 | 0.254625 | -0.093057 | 0.379672 | 1.749610 | 0.246200 | 2.366177 | -1.044515 | 0.923771 | 0.216054 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.406799 | 0.155707 | 0.338096 | -0.491174 | -0.339394 | -0.679146 | -0.314873 |
| 592 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.103362 | -1.488665 | -0.887515 | -0.891688 | 0.0 | -0.340410 | 0.254625 | -1.618459 | 0.379672 | 1.749610 | 1.153254 | 2.177708 | -0.187075 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.892537 | 0.155707 | -1.077862 | 3.100991 | 2.697683 | 0.252146 | -1.155935 |
| 595 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | -1.280421 | -0.887515 | 1.061787 | 0.0 | -0.332102 | 1.169781 | -1.716872 | 0.379672 | 2.653309 | -0.660853 | 2.707630 | 1.608898 | 1.724681 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 3.692454 | -0.620189 | 0.338096 | 3.917392 | 2.973780 | 3.356449 | 1.086895 |
| 609 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | -0.529253 | 0.593216 | -0.891688 | 0.0 | -0.302192 | -0.660531 | 1.383138 | 0.379672 | 1.749610 | -1.567907 | 2.264187 | -1.280764 | 1.324226 | 2.402628 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | -0.491174 | -0.891589 | -0.368715 | -1.155935 |
| 616 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 1.278013 | 2.073946 | 1.061787 | 0.0 | -0.288899 | -1.575686 | 0.005356 | 0.379672 | 1.749610 | 0.246200 | 2.083155 | -1.225391 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.278234 | -0.620189 | -1.077862 | 2.121310 | 0.488900 | 0.562576 | 3.610079 |
| 624 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.760373 | -0.350758 | -0.270544 | -0.891688 | 0.0 | -0.270622 | -1.575686 | 0.595834 | -1.026167 | 0.845911 | 1.153254 | 0.941508 | 0.899729 | 1.724681 | 0.762698 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 3.049627 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.034520 |
| 625 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.884358 | 0.316121 | -0.023755 | 0.085049 | 0.0 | -0.267298 | 1.169781 | 0.399008 | 0.379672 | 0.845911 | -1.567907 | 0.904749 | 0.692290 | 0.523316 | -0.877232 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.864013 | -0.620189 | 0.338096 | -0.327893 | -0.063296 | -0.679146 | -0.595227 |
| 627 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | 0.216957 | 1.950552 | 1.061787 | 0.0 | -0.262313 | 0.254625 | 0.743454 | -1.026167 | 1.749610 | 1.153254 | 1.555996 | 0.662636 | 0.122861 | 1.855984 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 2.535365 | 0.155707 | 0.338096 | 0.325228 | 1.041095 | -0.679146 | -1.155935 |
| 646 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 0.162417 | -0.147150 | 0.085049 | 0.0 | -0.212465 | -1.575686 | 0.349801 | 0.379672 | 1.749610 | 1.153254 | 1.133163 | 1.191210 | 0.923771 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.149668 | 0.155707 | 0.338096 | -0.817734 | -1.167687 | -0.058285 | -0.595227 |
| 649 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | -1.736576 | 1.703764 | 1.061787 | 0.0 | -0.205818 | 1.169781 | 0.300595 | 0.379672 | 1.749610 | 1.153254 | 1.651399 | 0.828193 | 1.324226 | 0.762698 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.792496 | -2.171982 | 0.338096 | 0.815068 | 1.317193 | 0.252146 | 1.086895 |
| 653 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.194645 | -0.887515 | 1.061787 | 0.0 | -0.199172 | -1.575686 | 1.579964 | 0.379672 | 1.749610 | -1.567907 | 2.426733 | -1.372959 | -0.678049 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.535365 | 0.155707 | 0.338096 | 3.917392 | 0.488900 | 3.666880 | 0.806541 |
| 674 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | 1.163974 | -0.517332 | 0.085049 | 0.0 | -0.141015 | 0.254625 | -1.421633 | -1.026167 | 0.845911 | -0.660853 | 0.860341 | -0.710871 | -0.277594 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.635406 | 0.155707 | 0.338096 | -0.164613 | -1.167687 | -0.679146 | -0.034520 |
| 677 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -0.682957 | -0.147150 | -0.891688 | 0.0 | -0.134368 | -1.575686 | -0.732742 | 0.379672 | 0.845911 | -0.660853 | 0.191245 | 1.147362 | 0.523316 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.278234 | 0.155707 | -1.077862 | 3.100991 | 1.317193 | -0.368715 | 0.806541 |
| 695 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.008343 | -0.440005 | -1.010909 | 1.061787 | 0.0 | -0.091166 | -1.575686 | -0.978775 | -1.026167 | 0.845911 | 0.246200 | 0.872452 | 0.085575 | 0.923771 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 0.735447 | -0.620189 | -2.493820 | 1.141629 | -0.891589 | 2.735589 | 0.806541 |
| 699 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | 0.621051 | -1.010909 | -0.891688 | 0.0 | -0.081196 | 1.169781 | 0.202182 | 0.379672 | 1.749610 | 1.153254 | 2.251438 | -0.068036 | -0.277594 | -0.057267 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.892537 | -0.620189 | -1.077862 | 0.325228 | 1.041095 | 1.493867 | 1.086895 |
| 701 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 1.421801 | -0.887515 | -0.891688 | 0.0 | -0.072888 | 0.254625 | -1.027981 | 0.379672 | 1.749610 | 0.246200 | 1.773999 | -0.052717 | 1.324226 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 1.754054 | 1.631469 | 2.421585 | 3.977310 | -0.595227 |
| 714 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.802025 | -1.010909 | -0.891688 | 0.0 | -0.046302 | 1.169781 | 0.005356 | 0.379672 | 1.749610 | 1.153254 | 2.315182 | -1.081899 | 2.525591 | 1.855984 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 2.663930 | -1.396086 | -1.077862 | -0.327893 | -0.063296 | -0.368715 | -0.314873 |
| 716 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | 0.093002 | -0.023755 | 0.085049 | 0.0 | -0.042979 | -1.575686 | -0.093057 | 0.379672 | 2.653309 | 0.246200 | 2.744389 | -1.486657 | -0.277594 | 0.489376 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.249709 | -0.620189 | 1.754054 | 1.794749 | 3.249878 | -0.679146 | 1.927956 |
| 721 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 0.338433 | 1.827158 | 0.085049 | 0.0 | -0.033009 | 1.169781 | 1.432345 | 0.379672 | 1.749610 | 0.246200 | 1.587230 | -1.426786 | 0.122861 | 0.762698 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.378275 | -0.620189 | 0.338096 | 0.815068 | 1.869389 | -0.368715 | 0.245834 |
| 723 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.586272 | -0.270544 | -0.891688 | 0.0 | -0.029685 | 1.169781 | 0.448214 | 0.379672 | 0.845911 | 0.246200 | 0.922597 | 1.390638 | 1.324226 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.221185 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | 0.806541 |
| 728 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 1.582942 | 0.963398 | 0.085049 | 0.0 | -0.019716 | 0.254625 | -0.486709 | 0.379672 | 0.845911 | 0.246200 | 0.900074 | -0.052015 | 2.125136 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.378275 | -0.620189 | 0.338096 | 0.488508 | 0.764998 | -0.679146 | 1.086895 |
| 736 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | 1.369739 | -0.640727 | 1.061787 | 0.0 | -0.001438 | 0.254625 | 0.595834 | -1.026167 | 0.845911 | 0.246200 | 0.955319 | 1.114756 | 1.724681 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 2.021103 | 0.155707 | 0.338096 | 1.304909 | 1.869389 | 0.562576 | 1.086895 |
| 738 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.834182 | -1.010909 | -1.868426 | 0.0 | 0.001885 | 1.169781 | -0.043851 | -1.026167 | 1.749610 | 1.153254 | 1.325669 | -1.019358 | -0.678049 | 0.216054 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 0.155707 | 0.338096 | 2.284590 | 0.488900 | 2.735589 | 1.086895 |
| 741 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -1.332482 | -0.517332 | -0.891688 | 0.0 | 0.006870 | 1.169781 | 1.186312 | 0.379672 | 2.653309 | 0.246200 | 2.507263 | -0.919574 | 1.324226 | -0.603911 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.249709 | 0.155707 | 1.754054 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 743 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 2.417384 | -0.216886 | -0.887515 | 0.085049 | 0.0 | 0.011855 | 0.254625 | 0.152975 | -1.026167 | 1.749610 | 1.153254 | 1.534748 | 1.056291 | 0.122861 | -0.603911 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 2.406799 | 0.931603 | 0.338096 | -0.327893 | -0.339394 | 0.562576 | -0.314873 |
| 745 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -1.654765 | 1.086793 | 1.061787 | 0.0 | 0.015178 | 0.254625 | -0.191470 | 0.379672 | -0.057788 | -0.660853 | -0.025058 | -0.185669 | 0.122861 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 0.735447 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 1.183437 | 0.806541 |
| 746 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -1.377106 | -0.270544 | -1.868426 | 0.0 | 0.016840 | -0.660531 | -0.535916 | -2.432006 | 2.653309 | 0.246200 | 2.862102 | 0.839155 | -0.678049 | 1.855984 | 2.346151 | -0.658973 | 0.0 | 1.415991 | 1.249709 | 0.155707 | 0.338096 | 2.284590 | 3.249878 | 0.873006 | 1.647603 |
| 749 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | -1.330003 | -0.887515 | -1.868426 | 0.0 | 0.021825 | -1.575686 | -0.437503 | -2.432006 | 2.653309 | 1.153254 | 2.834905 | 1.620844 | -0.678049 | -0.057267 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.792496 | 0.155707 | 0.338096 | 4.080672 | 2.697683 | 1.183437 | 1.367249 |
| 750 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.774856 | 1.600296 | 2.320735 | 0.085049 | 0.0 | 0.023487 | 1.169781 | -0.634329 | 1.785511 | 1.749610 | 1.153254 | 1.448482 | -0.362048 | 0.122861 | 0.762698 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.506840 | -0.620189 | 0.338096 | 0.815068 | 1.869389 | 2.735589 | 1.927956 |
| 755 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 1.069768 | 0.223033 | -0.891688 | 0.0 | 0.033456 | 1.169781 | 1.186312 | 0.379672 | 1.749610 | 1.153254 | 2.368514 | -1.252094 | 0.122861 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.892537 | 0.931603 | 1.754054 | 0.325228 | -0.339394 | -0.368715 | -0.875581 |
| 758 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.417384 | 0.710298 | -1.010909 | -0.891688 | 0.0 | 0.038441 | -0.660531 | 0.005356 | 0.379672 | 0.845911 | 1.153254 | 1.147612 | -0.460286 | 0.122861 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.349751 | -1.396086 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | -0.034520 |
| 760 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | -1.682035 | -0.887515 | 0.085049 | 0.0 | 0.041765 | 0.254625 | -1.372427 | -1.026167 | 0.845911 | -0.660853 | 0.217168 | 1.296335 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.406799 | -0.620189 | 0.338096 | 1.304909 | 0.764998 | 1.183437 | 2.208310 |
| 766 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 1.639962 | -0.887515 | 1.061787 | 0.0 | 0.060042 | -0.660531 | -0.191470 | 0.379672 | 2.653309 | 0.246200 | 2.705718 | -0.205204 | -0.277594 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.278234 | -0.620189 | -1.077862 | 0.161947 | -0.891589 | 1.493867 | 0.806541 |
| 770 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.923430 | -1.010909 | 1.061787 | 0.0 | 0.073335 | 1.169781 | -1.274014 | 0.379672 | 2.653309 | 1.153254 | 2.788584 | 1.002323 | 2.525591 | 0.489376 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 1.506840 | -2.171982 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 771 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.692944 | -0.887515 | 1.061787 | 0.0 | 0.074997 | 0.254625 | -1.224807 | -1.026167 | 0.845911 | 0.246200 | 0.888813 | -0.832157 | 1.324226 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.221185 | 0.931603 | 0.338096 | 0.325228 | -0.063296 | 1.493867 | -1.155935 |
| 774 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -0.888723 | -0.887515 | -1.868426 | 0.0 | 0.081644 | 0.254625 | -1.274014 | -1.026167 | 1.749610 | -1.567907 | 2.178558 | 0.423014 | 1.724681 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 2.535365 | 0.155707 | 1.754054 | 0.325228 | 0.764998 | 1.183437 | -0.595227 |
| 787 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | 0.715256 | -0.887515 | -1.868426 | 0.0 | 0.118199 | 1.169781 | -0.043851 | 0.379672 | 0.845911 | -0.660853 | 0.950432 | 0.210797 | 0.122861 | 0.762698 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.506840 | 0.931603 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.595227 |
| 789 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | 1.421801 | -1.010909 | -0.891688 | 0.0 | 0.121523 | -0.660531 | 1.235519 | -1.026167 | 0.845911 | -1.567907 | 0.845467 | -1.684679 | 2.525591 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.635406 | -1.396086 | 0.338096 | 2.121310 | 0.488900 | 0.252146 | 0.526188 |
| 799 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -0.826745 | -0.887515 | -0.891688 | 0.0 | 0.139801 | 1.169781 | -1.520046 | 0.379672 | 1.749610 | -1.567907 | 2.371701 | 0.012072 | -1.078504 | 0.489376 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 0.155707 | 0.338096 | 2.447870 | 0.488900 | 3.356449 | 0.806541 |
| 804 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | 1.139183 | -1.010909 | 1.061787 | 0.0 | 0.151432 | -1.575686 | -1.520046 | 1.785511 | 1.749610 | 1.153254 | 2.205968 | 0.258721 | -0.277594 | 1.855984 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 2.021103 | 0.155707 | -1.077862 | -0.327893 | -0.063296 | -0.058285 | -0.875581 |
| 806 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | 0.474783 | -0.270544 | 1.061787 | 0.0 | 0.154755 | -0.660531 | 1.038693 | 0.379672 | 0.845911 | -0.660853 | 0.837605 | 0.141791 | 1.724681 | 1.036019 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.864013 | 0.931603 | 0.338096 | 0.161947 | 0.488900 | 0.562576 | -1.155935 |
| 810 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.982929 | -0.764121 | -1.868426 | 0.0 | 0.164725 | -1.575686 | -0.683535 | 0.379672 | 1.749610 | 0.246200 | 2.329206 | 0.180299 | 0.122861 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 0.155707 | 0.338096 | 0.815068 | 1.317193 | 0.562576 | 1.367249 |
| 812 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 0.692944 | 2.197341 | 0.085049 | 0.0 | 0.168048 | 0.254625 | 0.841867 | 0.379672 | 0.845911 | -1.567907 | 0.917285 | -0.390437 | 2.125136 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.864013 | -1.396086 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | -0.875581 |
| 813 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -1.486186 | -0.887515 | 0.085049 | 0.0 | 0.169710 | -1.575686 | 0.891073 | 0.379672 | 1.749610 | 1.153254 | 1.203919 | -0.107669 | 1.724681 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 2.589994 | 1.249709 | 0.931603 | 0.338096 | 1.794749 | 0.764998 | 2.735589 | 0.245834 |
| 814 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 1.253222 | 0.593216 | 0.085049 | 0.0 | 0.171371 | 0.254625 | -1.077188 | -1.026167 | 2.653309 | 0.246200 | 2.788372 | 0.454073 | -0.678049 | -0.330589 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 1.754054 | 2.121310 | 0.764998 | 0.562576 | 1.367249 |
| 821 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.558080 | -0.147150 | 1.061787 | 0.0 | 0.187988 | 1.169781 | -0.486709 | -1.026167 | 1.749610 | -0.660853 | 1.405986 | -0.342091 | 1.324226 | 0.489376 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | 0.325228 | 1.041095 | -0.058285 | -0.314873 |
| 837 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | -0.519337 | -0.023755 | 1.061787 | 0.0 | 0.234513 | -0.660531 | 1.087899 | 0.379672 | 0.845911 | 0.246200 | 1.486515 | -0.074642 | 2.525591 | 0.489376 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.121144 | 0.155707 | -1.077862 | 1.794749 | 0.764998 | -0.058285 | 2.488664 |
| 838 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.555852 | -0.796996 | 0.346427 | 0.085049 | 0.0 | 0.236175 | 0.254625 | -1.077188 | 0.379672 | 1.749610 | -1.567907 | 1.541547 | -1.667673 | -1.078504 | -0.877232 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.378275 | -0.620189 | -1.077862 | 2.284590 | 1.317193 | 3.356449 | 2.769018 |
| 851 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | -0.209449 | -0.640727 | 1.061787 | 0.0 | 0.276054 | 1.169781 | 1.284725 | 0.379672 | 2.653309 | -1.567907 | 2.855728 | 0.598971 | 0.523316 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.149668 | -0.620189 | 0.338096 | -0.327893 | -0.615492 | 0.562576 | -0.595227 |
| 858 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 1.042498 | -0.270544 | -0.891688 | 0.0 | 0.292670 | 1.169781 | -0.781948 | 0.379672 | 2.653309 | 0.246200 | 2.571644 | 0.608106 | 0.122861 | 0.762698 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.892537 | 2.483396 | 0.338096 | -0.001333 | 0.764998 | 0.562576 | 0.806541 |
| 861 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 1.486257 | -0.887515 | 0.085049 | 0.0 | 0.297655 | 0.254625 | 0.152975 | 0.379672 | 1.749610 | -1.567907 | 2.240602 | 1.375038 | 2.125136 | 2.129306 | 2.346151 | -1.584178 | 0.0 | -0.932014 | 2.149668 | -0.620189 | 0.338096 | 3.100991 | 2.973780 | 3.977310 | 1.367249 |
| 867 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 1.533360 | -0.887515 | 0.085049 | 0.0 | 0.315933 | 1.169781 | -1.766079 | 0.379672 | 1.749610 | -1.567907 | 2.412285 | -0.677843 | -0.277594 | 1.855984 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 869 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 1.605254 | 0.716610 | -0.891688 | 0.0 | 0.319256 | 1.169781 | -0.683535 | 0.379672 | 2.653309 | -0.660853 | 2.672571 | -0.486848 | 0.923771 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.763972 | -0.620189 | 0.338096 | -0.491174 | -0.615492 | -0.679146 | -0.314873 |
| 890 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.405369 | -1.010909 | 1.061787 | 0.0 | 0.365782 | 1.169781 | -0.634329 | 0.379672 | 0.845911 | 0.246200 | 0.849717 | -0.654092 | 1.724681 | 0.489376 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.792496 | -0.620189 | -2.493820 | -0.327893 | -0.063296 | -0.368715 | -0.034520 |
| 894 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | -0.291259 | -0.764121 | 0.085049 | 0.0 | 0.374090 | 1.169781 | 0.940280 | 0.379672 | 1.749610 | 1.153254 | 2.395924 | 1.287481 | 0.122861 | -0.330589 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 3.178192 | -0.620189 | 0.338096 | 0.488508 | 1.317193 | -0.679146 | 1.367249 |
| 898 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 0.291330 | -0.764121 | 0.085049 | 0.0 | 0.382398 | 0.254625 | 1.481551 | -2.432006 | 2.653309 | 1.153254 | 2.812595 | 0.605998 | 0.122861 | -0.330589 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.763972 | -0.620189 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | 0.806541 |
| 899 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.732610 | -0.640727 | -0.891688 | 0.0 | 0.384060 | -1.575686 | 0.940280 | -1.026167 | 2.653309 | 0.246200 | 2.593954 | -0.307659 | -0.277594 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.506840 | -0.620189 | 1.754054 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 904 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | -0.216886 | -1.010909 | 0.085049 | 0.0 | 0.395691 | 1.169781 | 0.497421 | -1.026167 | 2.653309 | 1.153254 | 2.499189 | -0.784233 | 1.324226 | -0.877232 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.763972 | 0.155707 | 1.754054 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 907 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | 0.735089 | -0.517332 | 0.085049 | 0.0 | 0.402338 | -0.660531 | 1.087899 | 0.379672 | 2.653309 | -0.660853 | 2.488140 | -0.781703 | 1.724681 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.892537 | 1.707500 | 0.338096 | 2.447870 | 1.317193 | 0.252146 | 1.647603 |
| 913 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.884358 | 1.602775 | -0.887515 | 0.085049 | 0.0 | 0.418954 | -1.575686 | 1.383138 | -2.432006 | 2.653309 | -0.660853 | 2.617964 | -1.661208 | -0.277594 | 0.216054 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.892537 | -0.620189 | 0.338096 | 2.774431 | 1.593291 | -0.368715 | 1.927956 |
| 914 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -1.550643 | -0.147150 | -1.868426 | 0.0 | 0.420616 | 1.169781 | -1.421633 | -1.026167 | 1.749610 | -0.660853 | 1.503089 | 1.585147 | -0.678049 | -0.057267 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.921061 | 0.155707 | 0.338096 | 4.243953 | 1.317193 | 3.977310 | -1.155935 |
| 916 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -1.572955 | -0.640727 | -0.891688 | 0.0 | 0.423939 | 1.169781 | -1.618459 | -1.026167 | 2.653309 | -0.660853 | 2.610527 | -0.613757 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.892537 | -0.620189 | 0.338096 | 0.651788 | -0.063296 | -0.679146 | 1.086895 |
| 918 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.541369 | -1.401897 | -0.023755 | 0.085049 | 0.0 | 0.427262 | 1.169781 | 0.841867 | 0.379672 | 2.653309 | -0.660853 | 2.835330 | 0.686247 | 0.523316 | 2.402628 | 2.346151 | -1.584178 | 0.0 | 0.241988 | 2.535365 | 1.707500 | -1.077862 | 3.590832 | 1.593291 | 2.735589 | 1.647603 |
| 919 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.417384 | 1.553193 | 1.086793 | 1.061787 | 0.0 | 0.428924 | 1.169781 | 0.054562 | 0.379672 | 0.845911 | 1.153254 | 0.851842 | 0.799523 | 1.324226 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.763972 | 2.483396 | -1.077862 | 0.325228 | 0.764998 | 0.873006 | -0.034520 |
| 922 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | 0.982999 | -0.640727 | -0.891688 | 0.0 | 0.437232 | 0.254625 | 1.284725 | 1.785511 | 2.653309 | -1.567907 | 2.695731 | 0.444657 | -0.678049 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 1.892537 | 0.931603 | -1.077862 | 2.937711 | 1.317193 | 3.666880 | 2.488664 |
| 926 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | -0.673041 | -0.640727 | 1.061787 | 0.0 | 0.445540 | 1.169781 | -0.486709 | -1.026167 | 0.845911 | 1.153254 | 0.792135 | 0.850399 | 0.122861 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.506840 | 0.155707 | 1.754054 | 2.284590 | 0.764998 | 3.977310 | 3.610079 |
| 936 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | -1.087051 | 1.950552 | 0.085049 | 0.0 | 0.467141 | -0.660531 | 0.841867 | 0.379672 | 2.653309 | -0.660853 | 2.455843 | -0.179626 | 0.122861 | 1.855984 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 1.378275 | 0.931603 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 |
| 937 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0.227347 | -0.968054 | 0.469821 | 1.061787 | 0.0 | 0.468803 | 0.254625 | 1.383138 | -1.026167 | 1.749610 | -0.660853 | 2.256538 | 0.424560 | 1.324226 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 1.249709 | 0.931603 | 0.338096 | 1.958030 | 1.317193 | 3.977310 | -0.595227 |
| 945 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 1.287929 | 2.320735 | 0.085049 | 0.0 | 0.485419 | 1.169781 | -1.126394 | 0.379672 | 1.749610 | -1.567907 | 2.204906 | 1.139632 | 0.523316 | -1.150554 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.763972 | -0.620189 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.595227 |
| 947 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.650871 | 0.564031 | -0.517332 | 0.085049 | 0.0 | 0.488742 | -0.660531 | -0.093057 | 0.379672 | 0.845911 | -0.660853 | 0.412861 | 1.014832 | 2.525591 | 1.036019 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.164511 | -0.620189 | -1.077862 | 0.161947 | 0.764998 | 1.493867 | 0.806541 |
| 954 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.555852 | -0.762289 | -0.887515 | -1.868426 | 0.0 | 0.513667 | 0.254625 | -1.421633 | 0.379672 | 1.749610 | 0.246200 | 2.413347 | 1.724282 | -1.078504 | -0.603911 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.249709 | 0.155707 | -1.077862 | 2.121310 | 1.041095 | -0.058285 | 1.647603 |
| 955 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -1.290337 | -0.887515 | -0.891688 | 0.0 | 0.516990 | 1.169781 | -0.388296 | -2.432006 | 2.653309 | 0.246200 | 2.695094 | -1.028915 | 0.523316 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 1.707500 | 0.338096 | 1.958030 | 1.317193 | 2.114728 | 1.927956 |
| 956 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | -1.478749 | -0.147150 | 1.061787 | 0.0 | 0.520313 | 1.169781 | 1.629171 | 0.379672 | 2.653309 | -0.660853 | 2.807708 | -1.446321 | 1.324226 | -0.330589 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 3.178192 | 0.931603 | 0.338096 | -0.001333 | -0.339394 | 1.493867 | 0.806541 |
| 962 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.541369 | -0.080535 | -0.517332 | 0.085049 | 0.0 | 0.543576 | 0.254625 | 0.891073 | 0.379672 | 1.749610 | -0.660853 | 1.598491 | 0.460257 | -0.678049 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.792496 | -0.620189 | 0.338096 | 4.243953 | 1.317193 | -0.679146 | 1.647603 |
| 966 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | -0.499504 | -0.270544 | 1.061787 | 0.0 | 0.556869 | 0.254625 | -0.634329 | -1.026167 | 0.845911 | -1.567907 | 0.744752 | -0.321853 | 1.724681 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.535365 | -2.171982 | -1.077862 | 0.488508 | 1.317193 | 0.873006 | 1.367249 |
| 971 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 1.493695 | 0.223033 | -0.891688 | 0.0 | 0.568500 | 1.169781 | 0.792660 | -1.026167 | 1.749610 | -0.660853 | 1.410660 | 1.423103 | 0.122861 | 0.216054 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 2.278234 | -1.396086 | -1.077862 | -0.327893 | -0.615492 | -0.679146 | -0.314873 |
| 975 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -1.327524 | 0.469821 | 1.061787 | 0.0 | 0.576809 | -1.575686 | 0.940280 | 1.785511 | 1.749610 | 0.246200 | 1.528161 | -0.707779 | 1.324226 | 0.489376 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.635406 | -0.620189 | -1.077862 | 1.958030 | 0.764998 | 0.252146 | 1.086895 |
| 976 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.088878 | 1.404447 | 1.703764 | 0.085049 | 0.0 | 0.578470 | 1.169781 | 0.103769 | 0.379672 | 1.749610 | -0.660853 | 1.465905 | 0.551187 | 0.523316 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.792496 | -2.171982 | 0.338096 | 1.958030 | 3.249878 | 3.977310 | 1.367249 |
| 994 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.650871 | -1.191173 | 2.320735 | -0.891688 | 0.0 | 0.624996 | 1.169781 | -0.339090 | 1.785511 | 1.749610 | 0.246200 | 1.432971 | -0.643973 | -0.277594 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.635406 | 0.155707 | -1.077862 | -0.327893 | -0.339394 | -0.679146 | -0.595227 |
| 999 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | 0.854086 | 0.099639 | 0.085049 | 0.0 | 0.636627 | 0.254625 | -1.716872 | 0.379672 | 1.749610 | -1.567907 | 2.187695 | 0.323651 | -1.078504 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 1.707500 | 0.338096 | 2.121310 | 0.764998 | -0.679146 | 1.367249 |
| 1008 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.417764 | -1.010909 | 0.085049 | 0.0 | 0.659890 | 1.169781 | -0.585122 | 0.379672 | 1.749610 | 1.153254 | 2.300096 | -1.217240 | 1.324226 | 1.036019 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.278234 | 0.155707 | -1.077862 | 2.121310 | 0.764998 | 3.046019 | 0.806541 |
| 1009 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | 0.626009 | -1.010909 | 0.085049 | 0.0 | 0.661552 | 1.169781 | 0.497421 | 0.379672 | 2.653309 | -1.567907 | 2.804308 | 1.144410 | 0.122861 | 1.582663 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | 0.338096 | 0.325228 | 1.041095 | -0.368715 | 0.245834 |
| 1010 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | 0.826816 | -1.010909 | 1.061787 | 0.0 | 0.663213 | -0.660531 | 0.743454 | 1.785511 | 1.749610 | 1.153254 | 1.748501 | -0.266902 | -0.277594 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 2.535365 | 0.931603 | 1.754054 | -0.001333 | 0.764998 | -0.679146 | -1.155935 |
| 1024 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | -1.099446 | -0.887515 | 1.061787 | 0.0 | 0.694784 | -1.575686 | 0.792660 | 0.379672 | 1.749610 | 0.246200 | 2.266312 | 1.741288 | 0.122861 | 1.036019 | -0.426230 | -0.658973 | 0.0 | 1.415991 | 1.892537 | -0.620189 | 1.754054 | 2.121310 | 3.525976 | 0.873006 | 0.526188 |
| 1031 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -1.054823 | -0.023755 | 0.085049 | 0.0 | 0.718047 | -1.575686 | -0.683535 | 0.379672 | 0.845911 | 1.153254 | 0.763450 | 0.235110 | 0.523316 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.149668 | -1.396086 | 1.754054 | -0.001333 | 0.764998 | 0.562576 | -0.314873 |
| 1034 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.884358 | 0.583864 | 1.333581 | 0.085049 | 0.0 | 0.723032 | -0.660531 | 1.432345 | -2.432006 | 0.845911 | -1.567907 | 0.923872 | 0.779988 | -0.277594 | 0.762698 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.635406 | -0.620189 | 0.338096 | -0.001333 | 0.764998 | -0.679146 | 0.806541 |
| 1043 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | -0.881285 | -0.887515 | 0.085049 | 0.0 | 0.742971 | 1.169781 | -1.323220 | 1.785511 | 1.749610 | -0.660853 | 2.144987 | 0.766074 | 0.523316 | -0.877232 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 3.049627 | -0.620189 | -1.077862 | 0.325228 | 1.041095 | 1.804297 | 1.086895 |
| 1054 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 1.704418 | -0.270544 | 1.061787 | 0.0 | 0.762911 | 0.254625 | -1.520046 | 0.379672 | 0.845911 | -0.660853 | 0.842067 | 0.932475 | 0.122861 | -0.330589 | -0.426230 | -0.658973 | 0.0 | 1.415991 | 2.278234 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | 0.806541 |
| 1055 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | 0.065732 | 0.716610 | 0.085049 | 0.0 | 0.764573 | -0.660531 | 0.251388 | 0.379672 | 1.749610 | -1.567907 | 2.231890 | -0.335064 | 1.724681 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 0.606882 | 0.155707 | -1.077862 | 1.141629 | 1.041095 | 1.183437 | 1.367249 |
| 1076 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.613613 | 0.223033 | 1.061787 | 0.0 | 0.822730 | -0.660531 | 1.038693 | 0.379672 | 1.749610 | 1.153254 | 2.024724 | 1.425492 | 0.122861 | 1.309341 | 2.346151 | -1.584178 | 0.0 | 0.241988 | 1.892537 | -0.620189 | 0.338096 | 1.141629 | 1.317193 | -0.368715 | 2.208310 |
| 1078 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | -1.652286 | 2.320735 | 0.085049 | 0.0 | 0.827714 | 1.169781 | -1.667666 | 0.379672 | 1.749610 | -1.567907 | 2.087617 | 1.090724 | 0.122861 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.635406 | -1.396086 | 1.754054 | 2.121310 | 0.488900 | 3.666880 | 3.610079 |
| 1080 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -1.424209 | -0.764121 | 0.085049 | 0.0 | 0.834361 | 0.254625 | -0.732742 | 0.379672 | 1.749610 | -0.660853 | 2.146686 | -0.412221 | 2.125136 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | -0.620189 | 1.754054 | 0.978348 | 2.145487 | 0.873006 | -0.875581 |
| 1086 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | -1.163903 | 1.580370 | 2.038524 | 0.0 | 0.854300 | 0.254625 | 1.087899 | -2.432006 | 1.749610 | 1.153254 | 1.680296 | 1.424649 | -0.678049 | -0.603911 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.663930 | -0.620189 | 0.338096 | 4.080672 | 0.488900 | 3.356449 | 1.367249 |
| 1093 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.555852 | -1.275463 | -0.887515 | 0.085049 | 0.0 | 0.867593 | 1.169781 | -1.274014 | 0.379672 | 0.845911 | 1.153254 | 0.769400 | 0.604030 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.635406 | 0.155707 | -2.493820 | 2.121310 | 1.041095 | 3.356449 | 1.367249 |
| 1096 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.336849 | 0.236790 | -0.393938 | -0.891688 | 0.0 | 0.872578 | 0.254625 | -1.372427 | 0.379672 | 1.749610 | 1.153254 | 2.110778 | 0.431165 | -0.678049 | 1.582663 | 2.346151 | 1.191438 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 2.284590 | 0.764998 | 1.493867 | 0.806541 |
| 1103 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | -0.769726 | 0.840004 | 1.061787 | 0.0 | 0.884210 | 0.254625 | 1.481551 | 0.379672 | -0.057788 | 0.246200 | -0.013584 | -0.087150 | 2.125136 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.864013 | -0.620189 | 0.338096 | 0.161947 | 0.764998 | 1.493867 | 0.806541 |
| 1111 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | -0.484629 | -0.887515 | 2.038524 | 0.0 | 0.909134 | 0.254625 | 0.595834 | -1.026167 | 0.845911 | 1.153254 | 0.778961 | 0.042850 | -1.078504 | 0.216054 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.921061 | 0.931603 | 0.338096 | 4.243953 | 0.764998 | -0.368715 | 1.367249 |
| 1116 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -0.291259 | 2.073946 | 2.038524 | 0.0 | 0.919104 | 0.254625 | -0.289883 | -1.026167 | 2.653309 | 1.153254 | 2.779873 | 1.226065 | -0.678049 | 1.582663 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 3.178192 | 0.155707 | 0.338096 | 4.733793 | 0.488900 | -0.058285 | 2.488664 |
| 1126 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | -1.334961 | -0.023755 | 0.085049 | 0.0 | 0.940705 | 0.254625 | -0.339090 | 0.379672 | 2.653309 | 0.246200 | 2.725691 | 0.731642 | 0.523316 | 0.216054 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.021103 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 1129 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | 0.635925 | -0.023755 | -0.891688 | 0.0 | 0.947352 | 1.169781 | 1.333932 | -1.026167 | 2.653309 | 1.153254 | 2.785610 | 1.693363 | 2.125136 | 1.855984 | 2.346151 | 1.191438 | 0.0 | -0.932014 | 1.635406 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -0.875581 |
| 1135 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.593710 | -1.010909 | 1.061787 | 0.0 | 0.958983 | 1.169781 | -0.486709 | 1.785511 | 1.749610 | -1.567907 | 2.350878 | -1.568030 | -0.678049 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 2.021103 | 1.707500 | -2.493820 | 3.100991 | -1.167687 | -0.679146 | 2.208310 |
| 1138 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 1.069768 | 1.333581 | 2.038524 | 0.0 | 0.965630 | -0.660531 | -1.224807 | 0.379672 | 1.749610 | 0.246200 | 1.007588 | 0.896074 | -0.277594 | -0.057267 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | 0.338096 | 3.754112 | 1.041095 | 3.046019 | 2.488664 |
| 1140 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.774856 | 1.265617 | -0.270544 | 0.085049 | 0.0 | 0.968953 | -0.660531 | -1.716872 | 0.379672 | 2.653309 | 1.153254 | 2.665772 | -1.512797 | -1.078504 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 0.931603 | -1.077862 | 2.447870 | 0.764998 | -0.368715 | 1.647603 |
| 1154 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 0.925980 | 2.073946 | 1.061787 | 0.0 | 0.997200 | 1.169781 | 1.579964 | 0.379672 | 2.653309 | 0.246200 | 2.795171 | -1.277953 | 0.122861 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.021103 | -0.620189 | 0.338096 | -0.327893 | -0.615492 | -0.368715 | -1.155935 |
| 1156 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 0.202082 | 0.716610 | 0.085049 | 0.0 | 1.002185 | -1.575686 | 0.694247 | -1.026167 | 0.845911 | 0.246200 | 0.835481 | 1.614379 | -0.678049 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 0.864013 | -0.620189 | 0.338096 | 1.794749 | 2.973780 | 3.666880 | 2.208310 |
| 1162 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.210661 | -0.162346 | 0.099639 | 0.085049 | 0.0 | 1.020463 | 1.169781 | -0.535916 | -1.026167 | 0.845911 | -1.567907 | 0.808071 | 1.014269 | 2.525591 | 0.489376 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 0.478316 | 0.155707 | 0.338096 | 0.978348 | 2.145487 | 1.183437 | -1.155935 |
| 1166 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | -1.084572 | -0.640727 | 2.038524 | 0.0 | 1.028771 | 0.254625 | 1.137106 | -1.026167 | 1.749610 | 1.153254 | 1.848366 | -1.224267 | -0.277594 | 2.675949 | 2.346151 | -0.658973 | 0.0 | 0.241988 | 1.506840 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 1176 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.243234 | 1.580370 | 1.061787 | 0.0 | 1.047049 | -1.575686 | 0.300595 | 0.379672 | 1.749610 | -0.660853 | 2.105678 | -1.519965 | 0.122861 | 0.216054 | -0.426230 | -0.658973 | 0.0 | 1.415991 | 2.021103 | -0.620189 | 0.338096 | -0.491174 | -0.615492 | -0.368715 | -0.595227 |
| 1177 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.026066 | 0.963398 | 2.038524 | 0.0 | 1.048711 | 1.169781 | -0.781948 | -1.026167 | 0.845911 | -1.567907 | 1.437645 | 1.077653 | 0.923771 | -0.057267 | -0.426230 | 0.266233 | 0.0 | 2.589994 | 0.992578 | 0.155707 | 0.338096 | 1.141629 | 1.869389 | -0.368715 | 1.927956 |
| 1181 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.322365 | -0.836662 | -0.393938 | -1.868426 | 0.0 | 1.057019 | 0.254625 | -1.224807 | -1.026167 | 1.749610 | 0.246200 | 1.585743 | -0.373994 | -0.277594 | 1.036019 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.406799 | 0.155707 | 0.338096 | 1.304909 | 1.869389 | -0.058285 | 2.208310 |
| 1184 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | -0.541648 | 1.580370 | 2.038524 | 0.0 | 1.063666 | -0.660531 | 1.235519 | 0.379672 | 1.749610 | 0.246200 | 2.320919 | 0.614430 | 0.122861 | 2.675949 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 3.178192 | 2.483396 | 0.338096 | 0.488508 | 1.041095 | 0.562576 | 0.806541 |
| 1185 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 1.211077 | 0.716610 | -0.891688 | 0.0 | 1.065327 | 0.254625 | -0.043851 | -1.026167 | 1.749610 | 0.246200 | 2.358528 | -1.516170 | -0.678049 | 2.402628 | 2.346151 | -1.584178 | 0.0 | 0.241988 | 0.349751 | 0.155707 | 0.338096 | 1.141629 | 1.593291 | 1.183437 | 1.927956 |
| 1194 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 1.047456 | -0.887515 | 1.061787 | 0.0 | 1.081943 | -0.660531 | -0.929568 | 1.785511 | 1.749610 | -0.660853 | 2.011975 | 0.951869 | 1.324226 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 2.589994 | 2.278234 | -0.620189 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.595227 |
| 1195 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.016150 | -1.010909 | 0.085049 | 0.0 | 1.083605 | 0.254625 | -1.470840 | 0.379672 | 1.749610 | 0.246200 | 1.885975 | 1.134291 | 0.523316 | -0.330589 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.506840 | -0.620189 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | -1.155935 |
| 1221 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 1.079684 | -1.010909 | -1.868426 | 0.0 | 1.141762 | 0.254625 | 0.399008 | -1.026167 | 0.845911 | 0.246200 | 0.901986 | -1.534440 | 0.122861 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 1.763972 | 0.155707 | -1.077862 | 2.611150 | 2.973780 | 3.666880 | -0.034520 |
| 1223 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 0.720215 | -0.023755 | 0.085049 | 0.0 | 1.148409 | 0.254625 | 0.792660 | -2.432006 | 1.749610 | 0.246200 | 1.366890 | 1.384454 | 1.724681 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.763972 | 0.155707 | -2.493820 | 2.611150 | 0.212802 | 3.666880 | 1.647603 |
| 1225 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 0.502054 | 2.320735 | -0.891688 | 0.0 | 1.153393 | 1.169781 | -0.880361 | -1.026167 | 1.749610 | -0.660853 | 2.167509 | 0.394343 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 2.284590 | 0.488900 | 1.804297 | 0.526188 |
| 1242 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | -0.474713 | -0.270544 | 1.061787 | 0.0 | 1.188288 | -0.660531 | 1.087899 | 0.379672 | 2.653309 | -0.660853 | 2.832355 | -1.400364 | -0.678049 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.249709 | 0.155707 | -1.077862 | 2.284590 | 1.041095 | 3.046019 | 1.086895 |
| 1264 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -0.804433 | -0.887515 | 0.085049 | 0.0 | 1.238136 | 0.254625 | -0.289883 | -1.026167 | 2.653309 | -1.567907 | 2.663434 | 0.771836 | 2.125136 | -0.877232 | -0.426230 | -0.658973 | 0.0 | 2.589994 | 2.921061 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 1268 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.760373 | -0.350758 | -1.010909 | 1.061787 | 0.0 | 1.246445 | -1.575686 | -0.289883 | -1.026167 | 1.749610 | 0.246200 | 1.373052 | 1.123610 | 0.523316 | 1.309341 | 2.346151 | 1.191438 | 0.0 | 2.589994 | 2.021103 | -0.620189 | -1.077862 | -0.654454 | -0.615492 | -0.679146 | -0.595227 |
| 1275 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 0.345870 | -0.764121 | 0.085049 | 0.0 | 1.264722 | -1.575686 | -0.634329 | 0.379672 | 0.845911 | 0.246200 | 1.405136 | 1.218616 | -0.277594 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.478316 | -0.620189 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 1277 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.993860 | -0.169783 | -0.887515 | 1.061787 | 0.0 | 1.269707 | 0.254625 | -0.978775 | 0.379672 | 2.653309 | 1.153254 | 2.725053 | -0.013366 | 1.724681 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.635406 | 0.155707 | 0.338096 | -0.817734 | -0.891589 | -0.058285 | -0.595227 |
| 1278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.101159 | 1.439154 | 0.099639 | 0.085049 | 0.0 | 1.271369 | 1.169781 | 1.186312 | 0.379672 | 0.845911 | -1.567907 | 0.386301 | 1.635320 | 1.724681 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 0.478316 | -1.396086 | 0.338096 | 0.815068 | 1.041095 | 0.873006 | 0.806541 |
| 1295 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | -0.016079 | -0.640727 | -1.868426 | 0.0 | 1.312910 | 0.254625 | 0.743454 | 0.379672 | 0.845911 | 0.246200 | 0.838030 | 1.706855 | -1.078504 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 0.155707 | 1.754054 | 2.447870 | 2.697683 | 3.356449 | 0.245834 |
| 1301 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.307882 | -1.121758 | -0.887515 | 0.085049 | 0.0 | 1.327864 | -0.660531 | -0.683535 | 0.379672 | 1.749610 | -0.660853 | 2.079756 | 1.161416 | 0.523316 | 1.855984 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 3.306758 | -2.171982 | -1.077862 | 1.468189 | 1.317193 | 3.666880 | 2.769018 |
| 1303 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.103362 | 0.492137 | -0.640727 | 0.085049 | 0.0 | 1.332849 | 0.254625 | 1.284725 | -1.026167 | 0.845911 | -0.660853 | 0.813808 | 0.696787 | 2.125136 | -0.877232 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.149668 | 0.931603 | 0.338096 | 2.447870 | 1.869389 | 3.666880 | 1.647603 |
| 1310 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | 1.025144 | 0.716610 | 1.061787 | 0.0 | 1.349466 | -1.575686 | 1.038693 | 0.379672 | 1.749610 | 0.246200 | 1.972666 | 1.027480 | -0.277594 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.506840 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 1327 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 1.280492 | -0.764121 | 0.085049 | 0.0 | 1.392668 | -1.575686 | -1.027981 | 1.785511 | 1.749610 | -1.567907 | 1.428296 | -0.923931 | -0.277594 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.763972 | 1.707500 | 0.338096 | 1.958030 | 3.525976 | -0.058285 | 1.086895 |
| 1330 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 0.050857 | -0.393938 | 0.085049 | 0.0 | 1.397653 | -1.575686 | 0.743454 | -1.026167 | 2.653309 | 0.246200 | 2.738652 | 1.156075 | 1.724681 | -0.603911 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 1.468189 | 2.145487 | 1.183437 | 2.769018 |
| 1331 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | 1.044977 | 0.099639 | 0.085049 | 0.0 | 1.399314 | 1.169781 | 1.235519 | -1.026167 | 2.653309 | -0.660853 | 2.796659 | -0.102609 | 0.523316 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.278234 | 0.155707 | 0.338096 | 2.447870 | 1.593291 | 3.046019 | 1.367249 |
| 1348 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 0.829295 | -1.010909 | 1.061787 | 0.0 | 1.440855 | -1.575686 | 1.579964 | 0.379672 | 1.749610 | -1.567907 | 2.192794 | 0.657436 | -0.277594 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | 1.958030 | 0.764998 | 2.735589 | 3.329725 |
| 1351 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | -1.699389 | 1.580370 | 0.085049 | 0.0 | 1.454148 | 1.169781 | -0.388296 | 0.379672 | 1.749610 | 1.153254 | 2.267374 | -1.669079 | 0.122861 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.635406 | 0.155707 | 0.338096 | 2.447870 | 3.525976 | 0.562576 | 0.806541 |
| 1357 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | 1.471383 | -0.393938 | 0.085049 | 0.0 | 1.472426 | 0.254625 | 0.841867 | 0.379672 | 0.845911 | -1.567907 | 1.454431 | 0.074332 | 2.525591 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.864013 | 0.155707 | 1.754054 | 0.978348 | 0.764998 | 0.873006 | 0.806541 |
| 1374 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | -0.489587 | 1.456975 | 0.085049 | 0.0 | 1.517290 | 1.169781 | 0.300595 | 0.379672 | 1.749610 | 1.153254 | 2.416322 | -0.358675 | 0.523316 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.278234 | -0.620189 | -1.077862 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 1377 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.648321 | -0.887515 | -1.868426 | 0.0 | 1.522275 | -0.660531 | -1.175601 | 0.379672 | 2.653309 | 1.153254 | 2.689569 | -0.080826 | 0.122861 | -0.057267 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.149668 | 0.155707 | 0.338096 | -0.327893 | -0.063296 | 0.562576 | -0.314873 |
| 1401 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -1.520894 | 2.073946 | 1.061787 | 0.0 | 1.575447 | 0.254625 | 0.251388 | 1.785511 | 2.653309 | -0.660853 | 2.790497 | 1.615925 | 0.523316 | 0.762698 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 3.049627 | -2.171982 | 0.338096 | 0.488508 | 1.317193 | -0.368715 | -0.034520 |
| 1403 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -1.694431 | 0.716610 | 1.061787 | 0.0 | 1.578770 | -0.660531 | 0.546627 | 0.379672 | 1.749610 | -1.567907 | 1.452944 | 1.515719 | -1.078504 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.249709 | 0.155707 | 0.338096 | 2.121310 | 1.041095 | 2.735589 | 1.647603 |
| 1414 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 0.935897 | 1.950552 | 0.085049 | 0.0 | 1.608679 | -1.575686 | 0.891073 | 0.379672 | 0.845911 | 0.246200 | 0.452594 | -0.172739 | -0.277594 | 2.129306 | 2.346151 | -0.658973 | 0.0 | -0.932014 | 1.763972 | 0.155707 | 0.338096 | 1.631469 | 2.697683 | 3.046019 | 1.927956 |
| 1430 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.117845 | -1.491145 | 0.099639 | 0.085049 | 0.0 | 1.645235 | -0.660531 | 1.629171 | -2.432006 | 0.845911 | 0.246200 | 1.424259 | -1.537111 | 0.122861 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.121144 | 0.155707 | 0.338096 | 1.794749 | 3.249878 | -0.368715 | 1.927956 |
| 1437 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -1.729138 | -0.023755 | 0.085049 | 0.0 | 1.656867 | 1.169781 | 1.038693 | 0.379672 | 2.653309 | 1.153254 | 2.746939 | 0.138980 | -0.277594 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.249709 | 0.155707 | -1.077862 | -0.164613 | -1.167687 | -0.368715 | -0.314873 |
| 1443 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -1.245713 | -0.887515 | 0.085049 | 0.0 | 1.671821 | -1.575686 | -0.486709 | 0.379672 | 2.653309 | 0.246200 | 2.629863 | 0.421468 | 0.923771 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.635406 | -0.620189 | -1.077862 | 2.447870 | 0.488900 | 0.562576 | 2.769018 |
| 1445 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -0.546607 | 2.320735 | 1.061787 | 0.0 | 1.676806 | -1.575686 | -0.289883 | -1.026167 | 1.749610 | -0.660853 | 1.501601 | -1.218926 | -1.078504 | 2.129306 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 0.155707 | 0.338096 | 2.121310 | 0.764998 | -0.679146 | 1.647603 |
index_1 = np.where(y_var==1)
index_1
(array([ 0, 2, 3, 4, 6, 7, 10, 12, 13, 14, 16,
17, 19, 20, 21, 23, 24, 30, 31, 32, 33, 34,
35, 36, 37, 38, 39, 40, 41, 42, 47, 49, 50,
51, 52, 53, 54, 57, 66, 67, 68, 69, 70, 71,
72, 74, 77, 78, 79, 81, 82, 84, 86, 87, 96,
97, 99, 100, 101, 102, 104, 107, 108, 109, 113, 114,
115, 118, 122, 125, 127, 128, 130, 131, 132, 135, 136,
138, 140, 141, 142, 143, 145, 146, 148, 149, 156, 157,
159, 160, 161, 162, 163, 164, 169, 170, 171, 174, 175,
176, 177, 179, 180, 181, 182, 183, 184, 191, 192, 193,
196, 197, 198, 200, 202, 203, 204, 206, 207, 208, 209,
214, 215, 216, 217, 219, 221, 224, 225, 229, 230, 232,
234, 238, 239, 240, 241, 242, 245, 246, 248, 249, 250,
252, 253, 254, 255, 258, 259, 260, 262, 264, 265, 267,
272, 273, 274, 275, 284, 286, 287, 288, 289, 292, 294,
296, 298, 299, 301, 308, 309, 310, 312, 318, 320, 322,
323, 327, 328, 330, 331, 332, 333, 335, 336, 337, 340,
345, 346, 347, 348, 349, 350, 351, 352, 354, 356, 357,
358, 360, 361, 362, 363, 364, 365, 368, 369, 370, 371,
372, 373, 374, 377, 378, 380, 381, 382, 383, 385, 387,
388, 389, 391, 393, 395, 396, 397, 399, 402, 404, 405,
407, 409, 410, 413, 414, 415, 416, 418, 419, 421, 422,
428, 430, 431, 433, 436, 437, 438, 439, 440, 441, 443,
449, 450, 453, 454, 455, 456, 457, 458, 460, 461, 463,
464, 469, 470, 471, 472, 474, 476, 478, 479, 480, 481,
482, 483, 485, 486, 487, 488, 490, 493, 494, 495, 496,
498, 499, 500, 501, 504, 505, 507, 511, 512, 513, 515,
516, 517, 520, 521, 522, 525, 528, 536, 539, 540, 542,
543, 546, 547, 548, 549, 550, 551, 553, 554, 555, 556,
557, 559, 560, 565, 566, 570, 571, 572, 573, 574, 575,
576, 577, 579, 580, 581, 583, 585, 586, 587, 589, 591,
596, 597, 598, 599, 601, 602, 603, 605, 606, 613, 615,
617, 618, 620, 622, 623, 626, 628, 629, 630, 631, 632,
633, 634, 637, 638, 639, 640, 642, 643, 644, 645, 648,
650, 654, 655, 656, 657, 659, 660, 661, 662, 663, 665,
666, 667, 668, 669, 670, 671, 672, 673, 678, 679, 680,
682, 683, 684, 688, 689, 691, 694, 697, 698, 700, 703,
706, 709, 711, 712, 713, 715, 717, 719, 720, 722, 724,
725, 726, 727, 731, 732, 734, 735, 737, 739, 740, 742,
744, 747, 748, 754, 759, 761, 762, 763, 764, 765, 767,
769, 772, 775, 776, 777, 779, 781, 782, 786, 790, 793,
794, 795, 797, 798, 800, 801, 802, 803, 811, 815, 816,
818, 819, 820, 822, 823, 824, 826, 827, 828, 829, 830,
831, 832, 833, 835, 839, 840, 841, 842, 845, 848, 849,
850, 853, 854, 856, 857, 859, 860, 862, 863, 864, 865,
866, 868, 871, 872, 876, 877, 878, 880, 884, 885, 887,
892, 893, 895, 897, 901, 902, 903, 906, 909, 910, 911,
912, 915, 917, 921, 924, 925, 929, 931, 933, 934, 938,
939, 940, 946, 952, 953, 957, 961, 965, 967, 970, 972,
973, 974, 977, 980, 981, 982, 984, 986, 987, 988, 989,
990, 991, 993, 996, 998, 1000, 1001, 1002, 1003, 1004, 1006,
1011, 1012, 1013, 1014, 1015, 1016, 1017, 1019, 1020, 1021, 1022,
1023, 1025, 1026, 1027, 1028, 1032, 1035, 1036, 1037, 1038, 1039,
1041, 1042, 1044, 1045, 1046, 1047, 1049, 1051, 1052, 1056, 1057,
1059, 1060, 1061, 1062, 1064, 1065, 1066, 1067, 1068, 1069, 1070,
1071, 1072, 1074, 1075, 1077, 1079, 1082, 1083, 1088, 1091, 1092,
1097, 1098, 1100, 1101, 1102, 1104, 1105, 1107, 1108, 1109, 1110,
1112, 1113, 1114, 1115, 1117, 1118, 1120, 1121, 1123, 1125, 1127,
1128, 1132, 1133, 1134, 1136, 1137, 1139, 1141, 1144, 1145, 1147,
1151, 1152, 1153, 1158, 1161, 1164, 1165, 1167, 1168, 1169, 1170,
1171, 1172, 1173, 1175, 1178, 1180, 1182, 1183, 1189, 1191, 1192,
1193, 1196, 1197, 1199, 1200, 1201, 1202, 1203, 1205, 1207, 1209,
1211, 1213, 1215, 1217, 1219, 1222, 1224, 1226, 1227, 1228, 1229,
1230, 1233, 1234, 1236, 1237, 1238, 1241, 1243, 1245, 1246, 1247,
1248, 1249, 1250, 1252, 1254, 1255, 1256, 1257, 1258, 1259, 1260,
1261, 1262, 1263, 1266, 1270, 1271, 1272, 1273, 1276, 1279, 1283,
1284, 1285, 1286, 1287, 1290, 1292, 1293, 1294, 1296, 1297, 1299,
1302, 1305, 1306, 1307, 1308, 1309, 1311, 1312, 1313, 1315, 1317,
1319, 1320, 1321, 1323, 1324, 1325, 1326, 1329, 1332, 1335, 1336,
1337, 1338, 1339, 1341, 1342, 1343, 1344, 1345, 1349, 1352, 1353,
1354, 1355, 1356, 1358, 1359, 1360, 1362, 1365, 1366, 1367, 1369,
1371, 1375, 1376, 1378, 1379, 1380, 1381, 1382, 1383, 1387, 1388,
1390, 1391, 1394, 1396, 1397, 1400, 1402, 1406, 1407, 1408, 1411,
1413, 1415, 1417, 1419, 1420, 1422, 1423, 1426, 1427, 1428, 1433,
1434, 1435, 1436, 1438, 1440, 1442, 1448, 1449, 1452, 1453, 1454,
1455, 1456, 1457, 1458, 1459, 1460, 1461, 1464, 1465, 1467, 1469],
dtype=int64),)
cluster_1 = x.iloc[index_1]
cluster_1
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | 1.153254 | -0.108350 | 0.726020 | 2.125136 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.421642 | -2.171982 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | 0.245834 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | 0.246200 | -0.937654 | -1.674841 | 1.324226 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.550208 | 0.155707 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 |
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | 0.246200 | -0.763634 | 1.243211 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 0.252146 | -1.155935 |
| 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.660853 | -0.644858 | 0.325900 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.678774 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 6 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.417384 | 1.292887 | -0.764121 | 0.085049 | 0.0 | -1.686328 | 0.254625 | 0.743454 | 1.785511 | -0.961486 | -1.567907 | -0.814416 | -0.611227 | 0.523316 | 1.309341 | 2.346151 | -1.584178 | 0.0 | 2.589994 | 0.092620 | 0.155707 | -1.077862 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1461 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | -0.973012 | 2.320735 | 0.085049 | 0.0 | 1.711700 | 1.169781 | -1.323220 | -1.026167 | 0.845911 | -1.567907 | 0.924509 | 0.319435 | 0.523316 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.121144 | 0.155707 | 0.338096 | -0.654454 | -0.615492 | -0.058285 | -1.155935 |
| 1464 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | -1.196177 | 0.903668 | -0.517332 | 0.085049 | 0.0 | 1.720008 | 1.169781 | -1.766079 | -1.026167 | -0.961486 | 0.246200 | -0.751522 | 0.992907 | -1.078504 | 0.762698 | -0.426230 | 1.191438 | 0.0 | -0.932014 | -0.807339 | -0.620189 | 0.338096 | -0.491174 | -0.615492 | -0.679146 | -1.155935 |
| 1465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | 1.153254 | -0.835451 | -0.284329 | 0.523316 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.314873 |
| 1467 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.660853 | -0.076690 | -1.284418 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -0.678774 | -2.171982 | 0.338096 | -0.164613 | -0.615492 | -0.679146 | -0.314873 |
| 1469 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | 0.246200 | -0.445978 | -0.574124 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 1.754054 | -0.491174 | -0.339394 | -0.368715 | -0.595227 |
836 rows × 58 columns
index_2 = np.where(y_var==2)
index_2
(array([ 1, 5, 8, 9, 11, 15, 22, 26, 27, 43, 44,
46, 48, 55, 56, 58, 59, 60, 61, 64, 73, 75,
76, 80, 83, 88, 89, 91, 92, 94, 103, 111, 116,
117, 120, 121, 124, 129, 133, 134, 137, 139, 144, 150,
151, 152, 153, 154, 155, 166, 167, 168, 172, 173, 185,
188, 195, 199, 201, 205, 210, 211, 212, 220, 222, 223,
226, 227, 228, 236, 243, 247, 251, 256, 261, 266, 269,
276, 277, 278, 281, 282, 283, 285, 291, 293, 297, 302,
303, 304, 305, 306, 307, 313, 315, 317, 319, 321, 324,
325, 334, 338, 339, 341, 342, 343, 344, 353, 355, 359,
366, 376, 384, 386, 394, 398, 403, 412, 420, 423, 426,
432, 434, 435, 442, 444, 446, 447, 451, 452, 459, 462,
467, 468, 475, 484, 491, 503, 506, 508, 509, 514, 518,
519, 523, 524, 526, 527, 529, 530, 531, 532, 537, 541,
545, 558, 562, 563, 564, 567, 569, 578, 582, 590, 593,
594, 600, 604, 607, 608, 610, 611, 612, 614, 619, 621,
635, 636, 641, 647, 651, 652, 658, 664, 675, 676, 681,
685, 686, 687, 690, 692, 693, 696, 702, 704, 705, 707,
708, 710, 718, 729, 730, 733, 751, 752, 753, 756, 757,
768, 773, 778, 780, 783, 784, 785, 788, 791, 792, 796,
805, 807, 808, 809, 817, 825, 834, 836, 843, 844, 846,
847, 852, 855, 870, 873, 874, 875, 879, 881, 882, 883,
886, 888, 889, 891, 896, 900, 905, 908, 920, 923, 927,
928, 930, 932, 935, 941, 942, 943, 944, 948, 949, 950,
951, 958, 959, 960, 963, 964, 968, 969, 978, 979, 983,
985, 992, 995, 997, 1005, 1007, 1018, 1029, 1030, 1033, 1040,
1048, 1050, 1053, 1058, 1063, 1073, 1081, 1084, 1085, 1087, 1089,
1090, 1094, 1095, 1099, 1106, 1119, 1122, 1124, 1130, 1131, 1142,
1143, 1146, 1148, 1149, 1150, 1155, 1157, 1159, 1160, 1163, 1174,
1179, 1186, 1187, 1188, 1190, 1198, 1204, 1206, 1208, 1210, 1212,
1214, 1216, 1218, 1220, 1231, 1232, 1235, 1239, 1240, 1244, 1251,
1253, 1265, 1267, 1269, 1274, 1280, 1281, 1282, 1288, 1289, 1291,
1298, 1300, 1304, 1314, 1316, 1318, 1322, 1328, 1333, 1334, 1340,
1346, 1347, 1350, 1361, 1363, 1364, 1368, 1370, 1372, 1373, 1384,
1385, 1386, 1389, 1392, 1393, 1395, 1398, 1399, 1404, 1405, 1409,
1410, 1412, 1416, 1418, 1421, 1424, 1425, 1429, 1431, 1432, 1439,
1441, 1444, 1446, 1447, 1450, 1451, 1462, 1463, 1466, 1468],
dtype=int64),)
cluster_2 = x.iloc[index_2]
cluster_2
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.660853 | -0.291719 | 1.488876 | -0.678049 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | -0.164511 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 0.806541 |
| 5 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | -0.539166 | 0.502054 | -0.887515 | -0.891688 | 0.0 | -1.689652 | 1.169781 | 0.645041 | 0.379672 | -0.961486 | 1.153254 | -0.729850 | -0.344199 | -1.078504 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | -0.620189 | -1.077862 | -0.001333 | 0.764998 | 0.252146 | 0.526188 |
| 8 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.117845 | -1.453958 | 1.703764 | 0.085049 | 0.0 | -1.683005 | 1.169781 | -1.077188 | -1.026167 | 0.845911 | 0.246200 | 0.642338 | -0.776644 | -1.078504 | 1.582663 | 2.346151 | -0.658973 | 0.0 | -0.932014 | -0.164511 | -0.620189 | 0.338096 | 0.325228 | 0.764998 | -0.368715 | 1.086895 |
| 9 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 1.230910 | 2.197341 | 0.085049 | 0.0 | -1.681343 | 0.254625 | 1.383138 | 0.379672 | -0.057788 | 0.246200 | -0.268983 | 0.318170 | 1.324226 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 1.415991 | 0.735447 | 0.155707 | -1.077862 | -0.001333 | 0.764998 | 1.493867 | 0.806541 |
| 11 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | -0.867672 | -1.610141 | 0.716610 | -0.891688 | 0.0 | -1.678020 | 1.169781 | -0.831155 | -1.026167 | -0.057788 | 0.246200 | -0.490811 | -0.229237 | -1.078504 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | -0.164511 | 0.155707 | 0.338096 | 0.325228 | 0.212802 | -0.679146 | 1.086895 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1451 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.117845 | -1.134154 | 0.099639 | -0.891688 | 0.0 | 1.688438 | -1.575686 | 1.678377 | 0.379672 | -0.057788 | 1.153254 | -0.246461 | -1.170861 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | -0.164511 | -1.396086 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 1.367249 |
| 1462 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.199532 | 1.827158 | -1.868426 | 0.0 | 1.713362 | -0.660531 | -0.289883 | -1.026167 | 1.749610 | 1.153254 | 1.174597 | -0.770882 | -1.078504 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.249709 | -0.620189 | -1.077862 | 2.121310 | 1.317193 | 2.114728 | 0.526188 |
| 1463 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | -0.648668 | -1.183736 | -0.517332 | 0.085049 | 0.0 | 1.715024 | -0.660531 | 0.399008 | 0.379672 | -0.057788 | -1.567907 | 0.729454 | -1.479348 | -1.078504 | 1.036019 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.164511 | -0.620189 | 0.338096 | 0.325228 | -0.063296 | -0.368715 | 0.806541 |
| 1466 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | -1.567907 | 0.741140 | 1.004010 | 0.523316 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.293077 | 1.707500 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | 0.806541 |
| 1468 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.660853 | -0.236474 | -0.150393 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.735447 | 0.155707 | -1.077862 | 0.325228 | 0.488900 | -0.679146 | 1.086895 |
406 rows × 58 columns
WCSS_list = []
for k in range(1,10):
kmeans_model = KMeans(n_clusters=k)
kmeans_model.fit(X_principal)
WCSS = kmeans_model.inertia_
WCSS_list.append(WCSS)
print('K >>',k,'WCSS>>',WCSS)
K >> 1 WCSS>> 9899.510474678413 K >> 2 WCSS>> 5212.972971501031 K >> 3 WCSS>> 3686.2254848544867 K >> 4 WCSS>> 2494.5912560339857 K >> 5 WCSS>> 1860.692374538649 K >> 6 WCSS>> 1600.207985793003 K >> 7 WCSS>> 1410.9852036822958 K >> 8 WCSS>> 1250.0847066717606 K >> 9 WCSS>> 1132.191839323547
WCSS
1132.191839323547
WCSS_list
[9899.510474678413, 5212.972971501031, 3686.2254848544867, 2494.5912560339857, 1860.692374538649, 1600.207985793003, 1410.9852036822958, 1250.0847066717606, 1132.191839323547]
k = range(1,10)
plt.plot(k,WCSS_list)
plt.xlabel('Number of clusters(K)')
plt.ylabel('WCSS')
plt.title('Elbow method graph')
Text(0.5, 1.0, 'Elbow method graph')
kmeans_model = KMeans(n_clusters=5)
kmeans_model.fit(X_principal)
KMeans(n_clusters=5)
kmeans_model.inertia_
1860.680999645056
y_means = kmeans_model.fit_predict(X_principal)
y_means
array([0, 1, 0, ..., 4, 1, 4])
index_0 = np.where(y_means==0)
index_0
(array([ 0, 2, 4, 6, 19, 31, 33, 35, 36, 38, 52,
53, 54, 66, 78, 79, 87, 99, 104, 114, 115, 122,
130, 132, 135, 141, 142, 156, 157, 159, 174, 175, 183,
184, 192, 193, 196, 197, 198, 203, 204, 208, 215, 216,
219, 225, 230, 232, 234, 240, 242, 245, 248, 249, 250,
253, 254, 258, 265, 273, 275, 286, 287, 291, 298, 299,
308, 310, 322, 327, 330, 332, 333, 340, 346, 347, 352,
354, 356, 358, 360, 364, 365, 368, 371, 372, 378, 387,
388, 389, 391, 393, 395, 396, 409, 413, 428, 431, 433,
438, 439, 440, 441, 450, 454, 456, 458, 464, 469, 471,
472, 482, 483, 486, 488, 493, 499, 500, 504, 511, 520,
525, 528, 539, 542, 543, 547, 548, 549, 551, 554, 556,
557, 559, 560, 566, 570, 572, 573, 583, 587, 591, 596,
597, 599, 601, 603, 605, 617, 622, 623, 626, 628, 632,
639, 643, 648, 650, 654, 660, 661, 665, 667, 669, 672,
678, 679, 684, 691, 700, 703, 708, 713, 715, 717, 719,
735, 737, 742, 744, 747, 748, 759, 761, 767, 772, 775,
782, 790, 795, 801, 811, 816, 822, 824, 829, 830, 832,
839, 840, 845, 849, 854, 857, 862, 864, 865, 866, 877,
878, 884, 895, 897, 901, 903, 925, 931, 938, 943, 946,
953, 957, 961, 970, 973, 986, 988, 989, 990, 1000, 1002,
1006, 1011, 1013, 1014, 1015, 1019, 1020, 1023, 1026, 1028, 1035,
1036, 1037, 1038, 1042, 1044, 1046, 1049, 1051, 1057, 1062, 1065,
1066, 1067, 1071, 1074, 1075, 1077, 1079, 1083, 1088, 1092, 1098,
1101, 1105, 1107, 1109, 1112, 1113, 1114, 1117, 1120, 1121, 1123,
1128, 1133, 1144, 1145, 1147, 1158, 1161, 1164, 1165, 1167, 1171,
1172, 1173, 1183, 1191, 1192, 1196, 1199, 1200, 1202, 1203, 1207,
1211, 1219, 1226, 1228, 1229, 1234, 1236, 1237, 1241, 1243, 1246,
1250, 1254, 1255, 1257, 1259, 1261, 1262, 1263, 1270, 1279, 1284,
1285, 1286, 1287, 1290, 1292, 1293, 1294, 1296, 1299, 1306, 1308,
1315, 1319, 1321, 1324, 1325, 1326, 1335, 1336, 1342, 1344, 1352,
1354, 1355, 1356, 1358, 1359, 1360, 1362, 1366, 1369, 1371, 1378,
1380, 1388, 1391, 1394, 1397, 1406, 1419, 1422, 1427, 1434, 1435,
1440, 1442, 1448, 1452, 1453, 1454, 1455, 1456, 1457, 1459, 1465],
dtype=int64),)
Group_0 = x.iloc[index_0]
Group_0
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | 0.742527 | -1.010909 | -0.891688 | 0.0 | -1.701283 | -0.660531 | 1.383138 | 0.379672 | -0.057788 | 1.153254 | -0.108350 | 0.726020 | 2.125136 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.421642 | -2.171982 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | 0.245834 |
| 2 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.008343 | 1.414363 | -0.887515 | -0.891688 | 0.0 | -1.696298 | 1.169781 | 1.284725 | -1.026167 | -0.961486 | 0.246200 | -0.937654 | -1.674841 | 1.324226 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.550208 | 0.155707 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 |
| 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -1.086676 | -0.524295 | -0.887515 | -1.868426 | 0.0 | -1.691313 | -1.575686 | -1.274014 | 0.379672 | -0.961486 | -0.660853 | -0.644858 | 0.325900 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.678774 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 6 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.417384 | 1.292887 | -0.764121 | 0.085049 | 0.0 | -1.686328 | 0.254625 | 0.743454 | 1.785511 | -0.961486 | -1.567907 | -0.814416 | -0.611227 | 0.523316 | 1.309341 | 2.346151 | -1.584178 | 0.0 | 2.589994 | 0.092620 | 0.155707 | -1.077862 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 19 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.117845 | -1.069697 | -0.887515 | 0.085049 | 0.0 | -1.663066 | 1.169781 | -1.027981 | 0.379672 | -0.961486 | 1.153254 | -0.543718 | -1.406408 | 0.923771 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.595227 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1455 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 1.287929 | -0.887515 | 1.061787 | 0.0 | 1.700069 | 0.254625 | -0.683535 | -1.026167 | -0.961486 | 0.246200 | -0.784882 | -1.628603 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | -0.421642 | -0.620189 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 1456 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.210661 | 0.982999 | 1.086793 | 1.061787 | 0.0 | 1.701731 | 0.254625 | 0.694247 | 0.379672 | -0.057788 | 0.246200 | -0.172943 | 1.444887 | -0.678049 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 1.415991 | -0.164511 | -0.620189 | 1.754054 | 0.488508 | -0.615492 | -0.679146 | -0.595227 |
| 1457 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 0.970604 | -0.887515 | 1.061787 | 0.0 | 1.705054 | 0.254625 | 1.579964 | 0.379672 | -0.961486 | 0.246200 | -0.956564 | -0.247929 | -0.277594 | -0.330589 | -0.426230 | -0.658973 | 0.0 | 2.589994 | 1.121144 | -0.620189 | 0.338096 | -0.327893 | -0.339394 | -0.679146 | -0.595227 |
| 1459 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.867672 | 1.426759 | 0.469821 | -0.891688 | 0.0 | 1.708377 | 1.169781 | -0.978775 | -1.026167 | -0.057788 | -0.660853 | -0.526508 | 1.316292 | 0.523316 | -0.603911 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.164511 | -0.620189 | 0.338096 | -0.491174 | -0.339394 | -0.679146 | -0.314873 |
| 1465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 0.202082 | 1.703764 | -0.891688 | 0.0 | 1.721670 | 0.254625 | -1.224807 | 1.785511 | -0.057788 | 1.153254 | -0.835451 | -0.284329 | 0.523316 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.314873 |
374 rows × 58 columns
index_1 = np.where(y_means==1)
index_1
(array([ 1, 5, 8, 9, 11, 15, 22, 26, 27, 43, 44,
46, 48, 56, 58, 59, 60, 61, 73, 75, 76, 80,
83, 88, 89, 91, 92, 94, 103, 111, 116, 117, 120,
121, 124, 129, 133, 134, 137, 139, 144, 150, 151, 152,
153, 154, 155, 158, 166, 167, 168, 172, 173, 185, 188,
195, 199, 201, 205, 210, 211, 212, 220, 222, 223, 226,
227, 228, 236, 243, 247, 256, 261, 266, 269, 271, 276,
277, 281, 282, 283, 285, 293, 297, 303, 304, 305, 306,
307, 313, 315, 317, 319, 321, 324, 325, 334, 338, 339,
341, 342, 343, 344, 353, 355, 359, 361, 366, 374, 384,
386, 394, 398, 403, 412, 420, 423, 426, 432, 434, 435,
442, 444, 446, 447, 451, 452, 459, 462, 467, 468, 484,
491, 502, 503, 506, 508, 509, 514, 518, 519, 523, 524,
526, 527, 529, 530, 531, 532, 537, 540, 545, 558, 562,
564, 567, 569, 578, 582, 590, 593, 594, 600, 604, 607,
608, 610, 611, 612, 619, 621, 636, 641, 647, 651, 652,
658, 664, 675, 676, 685, 686, 687, 690, 692, 693, 695,
696, 702, 704, 705, 707, 710, 718, 729, 730, 751, 752,
753, 756, 757, 768, 773, 778, 780, 783, 784, 785, 788,
791, 792, 796, 805, 807, 809, 817, 825, 836, 843, 844,
846, 847, 852, 855, 870, 872, 873, 874, 879, 881, 882,
883, 886, 888, 889, 891, 896, 900, 905, 908, 920, 923,
927, 928, 930, 932, 935, 941, 942, 944, 948, 949, 950,
951, 958, 959, 960, 963, 964, 968, 969, 978, 979, 983,
985, 992, 995, 997, 1005, 1007, 1018, 1029, 1030, 1033, 1040,
1048, 1050, 1053, 1058, 1063, 1073, 1081, 1084, 1085, 1087, 1089,
1090, 1094, 1095, 1099, 1106, 1119, 1122, 1124, 1130, 1131, 1142,
1143, 1146, 1148, 1150, 1155, 1157, 1159, 1160, 1162, 1163, 1174,
1179, 1186, 1187, 1188, 1190, 1204, 1206, 1208, 1210, 1212, 1214,
1216, 1218, 1220, 1232, 1240, 1244, 1251, 1253, 1260, 1265, 1267,
1269, 1274, 1280, 1281, 1282, 1288, 1289, 1291, 1298, 1300, 1304,
1314, 1316, 1318, 1322, 1328, 1333, 1334, 1340, 1341, 1346, 1347,
1350, 1361, 1363, 1364, 1368, 1372, 1373, 1384, 1385, 1386, 1389,
1392, 1393, 1395, 1398, 1399, 1404, 1405, 1409, 1410, 1412, 1416,
1418, 1421, 1424, 1425, 1429, 1431, 1432, 1439, 1441, 1444, 1446,
1447, 1450, 1451, 1463, 1466, 1468], dtype=int64),)
Group_1 = x.iloc[index_1]
Group_1
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.297775 | -0.147150 | -1.868426 | 0.0 | -1.699621 | 0.254625 | -0.240677 | -1.026167 | -0.057788 | -0.660853 | -0.291719 | 1.488876 | -0.678049 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | -0.164511 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 0.806541 |
| 5 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | -0.539166 | 0.502054 | -0.887515 | -0.891688 | 0.0 | -1.689652 | 1.169781 | 0.645041 | 0.379672 | -0.961486 | 1.153254 | -0.729850 | -0.344199 | -1.078504 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | -0.620189 | -1.077862 | -0.001333 | 0.764998 | 0.252146 | 0.526188 |
| 8 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.117845 | -1.453958 | 1.703764 | 0.085049 | 0.0 | -1.683005 | 1.169781 | -1.077188 | -1.026167 | 0.845911 | 0.246200 | 0.642338 | -0.776644 | -1.078504 | 1.582663 | 2.346151 | -0.658973 | 0.0 | -0.932014 | -0.164511 | -0.620189 | 0.338096 | 0.325228 | 0.764998 | -0.368715 | 1.086895 |
| 9 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 1.230910 | 2.197341 | 0.085049 | 0.0 | -1.681343 | 0.254625 | 1.383138 | 0.379672 | -0.057788 | 0.246200 | -0.268983 | 0.318170 | 1.324226 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 1.415991 | 0.735447 | 0.155707 | -1.077862 | -0.001333 | 0.764998 | 1.493867 | 0.806541 |
| 11 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | -0.867672 | -1.610141 | 0.716610 | -0.891688 | 0.0 | -1.678020 | 1.169781 | -0.831155 | -1.026167 | -0.057788 | 0.246200 | -0.490811 | -0.229237 | -1.078504 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | -0.164511 | 0.155707 | 0.338096 | 0.325228 | 0.212802 | -0.679146 | 1.086895 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1450 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | -0.210661 | 0.851607 | 2.073946 | 1.061787 | 0.0 | 1.686776 | 0.254625 | -1.716872 | 0.379672 | 0.845911 | 1.153254 | 0.495940 | 0.327305 | -0.678049 | 0.216054 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.293077 | -0.620189 | 0.338096 | 0.325228 | -1.167687 | -0.368715 | 0.806541 |
| 1451 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.117845 | -1.134154 | 0.099639 | -0.891688 | 0.0 | 1.688438 | -1.575686 | 1.678377 | 0.379672 | -0.057788 | 1.153254 | -0.246461 | -1.170861 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | -0.164511 | -1.396086 | 0.338096 | 0.488508 | 0.764998 | -0.368715 | 1.367249 |
| 1463 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | -0.648668 | -1.183736 | -0.517332 | 0.085049 | 0.0 | 1.715024 | -0.660531 | 0.399008 | 0.379672 | -0.057788 | -1.567907 | 0.729454 | -1.479348 | -1.078504 | 1.036019 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.164511 | -0.620189 | 0.338096 | 0.325228 | -0.063296 | -0.368715 | 0.806541 |
| 1466 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.469754 | -0.393938 | -1.868426 | 0.0 | 1.723332 | 1.169781 | -1.175601 | -1.026167 | 0.845911 | -1.567907 | 0.741140 | 1.004010 | 0.523316 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.293077 | 1.707500 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | 0.806541 |
| 1468 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.546677 | -0.887515 | 0.085049 | 0.0 | 1.728317 | 1.169781 | -0.142264 | -1.026167 | -0.057788 | -0.660853 | -0.236474 | -0.150393 | -0.277594 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.735447 | 0.155707 | -1.077862 | 0.325228 | 0.488900 | -0.679146 | 1.086895 |
391 rows × 58 columns
index_2 = np.where(y_means==2)
index_2
(array([ 18, 25, 28, 45, 55, 62, 63, 64, 90, 93, 98,
110, 119, 123, 126, 178, 186, 187, 190, 194, 218, 231,
235, 237, 244, 251, 257, 263, 268, 270, 295, 300, 311,
314, 326, 390, 400, 411, 417, 425, 427, 445, 448, 466,
473, 477, 533, 535, 538, 544, 561, 584, 592, 595, 616,
635, 649, 653, 677, 681, 699, 701, 716, 736, 738, 746,
749, 750, 760, 799, 810, 813, 814, 837, 838, 858, 861,
875, 894, 907, 913, 914, 918, 922, 926, 937, 954, 955,
962, 975, 976, 999, 1008, 1009, 1024, 1043, 1055, 1076, 1078,
1086, 1093, 1096, 1111, 1116, 1135, 1138, 1140, 1156, 1177, 1181,
1184, 1185, 1221, 1223, 1225, 1242, 1295, 1301, 1303, 1327, 1330,
1331, 1348, 1351, 1403, 1414, 1430, 1443, 1445, 1462], dtype=int64),)
Group_2 = x.iloc[index_2]
Group_2
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 18 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 1.032581 | -0.887515 | 1.061787 | 0.0 | -1.664727 | -1.575686 | 0.595834 | -1.026167 | 1.749610 | 1.153254 | 1.896174 | 1.083275 | -0.277594 | 0.216054 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.535365 | 0.155707 | 0.338096 | 2.937711 | 1.041095 | 0.252146 | 0.806541 |
| 25 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 1.188765 | -0.517332 | 0.085049 | 0.0 | -1.649772 | 0.254625 | -0.388296 | 0.379672 | 2.653309 | 0.246200 | 2.675333 | -0.502870 | 0.523316 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.892537 | 0.155707 | -1.077862 | 1.141629 | 2.421585 | 0.562576 | 1.086895 |
| 28 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | -0.806912 | -0.270544 | 1.061787 | 0.0 | -1.643126 | -1.575686 | -1.175601 | -1.026167 | 0.845911 | 1.153254 | 0.795747 | -1.717284 | 0.122861 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.635406 | 0.931603 | 0.338096 | 2.447870 | 0.488900 | 0.873006 | 3.610079 |
| 45 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | 1.382135 | 0.346427 | 0.085049 | 0.0 | -1.606570 | -0.660531 | -0.831155 | 0.379672 | 2.653309 | 0.246200 | 2.771161 | 0.276429 | -0.678049 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.506840 | -2.171982 | 0.338096 | 2.447870 | 2.973780 | 3.977310 | 1.086895 |
| 55 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | -0.429664 | -0.712706 | -1.010909 | -0.891688 | 0.0 | -1.581646 | -1.575686 | 1.579964 | 0.379672 | 0.845911 | 1.153254 | 1.477804 | 0.117056 | -0.678049 | -0.877232 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 0.478316 | -1.396086 | 0.338096 | 1.304909 | 2.697683 | 1.804297 | 2.208310 |
| 62 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 0.462388 | -0.270544 | -0.891688 | 0.0 | -1.570014 | -0.660531 | -1.126394 | -1.026167 | 2.653309 | 0.246200 | 2.600116 | 0.335597 | 0.923771 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.278234 | -0.620189 | -1.077862 | 3.264271 | -0.339394 | 3.356449 | 1.086895 |
| 63 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.417384 | 1.568068 | 1.950552 | 0.085049 | 0.0 | -1.568353 | -1.575686 | 1.629171 | 0.379672 | 0.845911 | -1.567907 | 0.240965 | -1.680744 | 1.724681 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.149668 | 0.155707 | -1.077862 | 2.284590 | 3.249878 | 1.493867 | 1.367249 |
| 64 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | 1.042498 | -0.147150 | 0.085049 | 0.0 | -1.565029 | 0.254625 | -0.339090 | 0.379672 | 0.845911 | 0.246200 | 0.763450 | -0.858860 | -0.678049 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 2.589994 | 0.735447 | -0.620189 | 0.338096 | 1.631469 | 2.697683 | 3.046019 | 1.086895 |
| 90 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | -0.675520 | -1.010909 | 1.061787 | 0.0 | -1.505211 | 0.254625 | 0.595834 | -1.026167 | 1.749610 | -0.660853 | 1.487365 | -0.027842 | -0.678049 | 1.855984 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 1.378275 | 0.155707 | -1.077862 | 2.447870 | -0.339394 | 2.735589 | 1.927956 |
| 93 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.993860 | -0.407777 | -1.010909 | 0.085049 | 0.0 | -1.496903 | 0.254625 | -1.274014 | -1.026167 | 0.845911 | -1.567907 | 0.886051 | -1.569997 | -0.277594 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 1.707500 | -1.077862 | 0.488508 | 1.317193 | 2.114728 | 0.245834 |
| 98 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.307882 | -0.298696 | 0.099639 | 1.061787 | 0.0 | -1.485271 | 1.169781 | -1.421633 | 0.379672 | 1.749610 | 0.246200 | 1.565770 | 1.418887 | -1.078504 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 3.435323 | -1.396086 | -1.077862 | 4.897073 | 1.593291 | -0.368715 | 1.086895 |
| 110 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 1.620129 | -1.010909 | 1.061787 | 0.0 | -1.462009 | -1.575686 | -1.766079 | -1.026167 | 0.845911 | -1.567907 | 0.208456 | 1.613817 | 0.122861 | 1.309341 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 1.506840 | -1.396086 | -1.077862 | 0.978348 | 2.145487 | 3.046019 | 1.086895 |
| 119 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.665354 | -1.012678 | 2.073946 | -0.891688 | 0.0 | -1.440407 | 0.254625 | 1.284725 | 0.379672 | 1.749610 | 1.153254 | 2.221691 | 0.728128 | -0.678049 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 1.763972 | 0.155707 | 1.754054 | 2.937711 | 2.145487 | 0.562576 | 2.208310 |
| 123 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | -0.293738 | -0.393938 | 0.085049 | 0.0 | -1.433761 | -1.575686 | -0.732742 | 0.379672 | 2.653309 | 0.246200 | 2.769461 | -1.103401 | 1.724681 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.506840 | 1.707500 | 0.338096 | 2.121310 | 3.802074 | 3.977310 | 3.049371 |
| 126 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.307882 | -1.625016 | 1.703764 | 1.061787 | 0.0 | -1.428776 | 1.169781 | 1.383138 | 0.379672 | 0.845911 | 1.153254 | 0.809346 | -1.524603 | -0.678049 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 3.692454 | 0.155707 | -1.077862 | 5.386914 | 1.593291 | 3.977310 | 0.526188 |
| 178 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.685436 | -1.010909 | -0.891688 | 0.0 | -1.297507 | -0.660531 | 1.284725 | 0.379672 | 0.845911 | -1.567907 | 0.839305 | -1.711241 | -0.678049 | 2.675949 | 2.346151 | 0.266233 | 0.0 | 2.589994 | 1.635406 | -0.620189 | 0.338096 | 2.774431 | 2.421585 | 3.977310 | 0.806541 |
| 186 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 0.462388 | -0.640727 | -1.868426 | 0.0 | -1.282553 | 1.169781 | -0.978775 | 0.379672 | 2.653309 | 0.246200 | 2.662372 | -1.098201 | -0.678049 | -0.330589 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.249709 | -0.620189 | 0.338096 | 2.121310 | 1.041095 | 2.114728 | 1.367249 |
| 187 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -0.273905 | 0.593216 | 1.061787 | 0.0 | -1.280891 | 0.254625 | -0.240677 | 1.785511 | 2.653309 | -0.660853 | 2.596291 | -0.136901 | 2.125136 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 3.178192 | 0.155707 | 0.338096 | 2.774431 | 2.973780 | -0.058285 | 3.049371 |
| 190 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | -0.256552 | -1.010909 | 1.061787 | 0.0 | -1.272583 | 0.254625 | -0.043851 | -1.026167 | 2.653309 | 0.246200 | 2.867626 | -1.213586 | -1.078504 | -0.330589 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.921061 | 1.707500 | 0.338096 | 4.243953 | 3.802074 | 2.735589 | 1.367249 |
| 194 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 0.973083 | -0.887515 | -0.891688 | 0.0 | -1.264275 | -1.575686 | -0.043851 | -1.026167 | 1.749610 | 1.153254 | 2.186207 | 0.864172 | 2.525591 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 1.378275 | -1.396086 | 0.338096 | 2.121310 | 1.041095 | 2.735589 | 1.086895 |
| 218 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 0.618571 | -0.393938 | 0.085049 | 0.0 | -1.201133 | 1.169781 | -0.437503 | -1.026167 | 0.845911 | 1.153254 | 0.501889 | 0.355132 | 1.324226 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.506840 | -0.620189 | 0.338096 | 1.958030 | 0.764998 | 3.046019 | 1.086895 |
| 231 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -0.670562 | -0.640727 | -0.891688 | 0.0 | -1.172885 | 0.254625 | -0.388296 | 0.379672 | 2.653309 | 1.153254 | 2.704655 | -1.318289 | -0.678049 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.378275 | 0.155707 | 0.338096 | 2.447870 | 3.525976 | 2.735589 | 3.049371 |
| 235 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.665354 | 0.573948 | 0.840004 | 0.085049 | 0.0 | -1.159592 | 1.169781 | 0.694247 | 0.379672 | 1.749610 | 1.153254 | 2.031523 | -0.923228 | 0.923771 | 1.855984 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | 1.631469 | 2.421585 | -0.368715 | 1.367249 |
| 237 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.650871 | -0.078056 | -0.887515 | 1.061787 | 0.0 | -1.156269 | -1.575686 | 0.645041 | -1.026167 | 2.653309 | 0.246200 | 2.669809 | 0.943999 | -0.678049 | 0.762698 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.792496 | -0.620189 | 1.754054 | 4.243953 | 0.764998 | 3.977310 | 2.208310 |
| 244 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | -1.364710 | -1.010909 | 0.085049 | 0.0 | -1.144638 | 0.254625 | 0.202182 | 1.785511 | 2.653309 | 1.153254 | 2.698281 | 0.232862 | -1.078504 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.763972 | -0.620189 | 0.338096 | 2.774431 | -1.167687 | -0.368715 | 0.806541 |
| 251 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.737497 | -0.887515 | 1.061787 | 0.0 | -1.133006 | 0.254625 | -0.093057 | 0.379672 | 0.845911 | 0.246200 | 0.942357 | -1.109304 | -1.078504 | 2.675949 | 2.346151 | 1.191438 | 0.0 | -0.932014 | 1.121144 | -1.396086 | 0.338096 | 1.958030 | 0.488900 | 2.735589 | 1.086895 |
| 257 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 1.520965 | -0.887515 | -0.891688 | 0.0 | -1.118052 | -1.575686 | -0.831155 | 0.379672 | 2.653309 | 0.246200 | 2.748001 | -1.175499 | -1.078504 | 1.036019 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.378275 | 1.707500 | 0.338096 | 2.284590 | 0.764998 | 0.252146 | 1.367249 |
| 263 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.993860 | -0.779642 | -0.887515 | 0.085049 | 0.0 | -1.099774 | 0.254625 | 0.448214 | -2.432006 | 1.749610 | -0.660853 | 2.203206 | 0.093305 | 0.122861 | -0.877232 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.149668 | -0.620189 | -1.077862 | -0.001333 | 0.764998 | 1.493867 | 0.806541 |
| 268 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.884358 | 1.444112 | 1.333581 | -0.891688 | 0.0 | -1.084819 | 0.254625 | 0.645041 | 0.379672 | 1.749610 | 1.153254 | 1.485878 | -0.957379 | -1.078504 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 2.121310 | 0.764998 | 0.562576 | 1.647603 |
| 270 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -0.868890 | -1.010909 | 0.085049 | 0.0 | -1.081496 | 1.169781 | 0.743454 | 0.379672 | 2.653309 | -1.567907 | 2.664922 | 0.649987 | -1.078504 | -0.330589 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 3.306758 | -0.620189 | 0.338096 | 4.733793 | 1.593291 | 0.562576 | 2.488664 |
| 295 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -0.613542 | 2.073946 | 0.085049 | 0.0 | -1.031647 | 0.254625 | 0.546627 | 0.379672 | 1.749610 | -0.660853 | 1.492040 | 0.077424 | 0.923771 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | -0.620189 | 1.754054 | 2.121310 | -0.063296 | 0.562576 | 1.086895 |
| 300 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -1.161424 | -0.887515 | 1.061787 | 0.0 | -1.021677 | 1.169781 | 1.087899 | 0.379672 | 1.749610 | -0.660853 | 2.021112 | 0.222462 | -0.678049 | 1.036019 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.378275 | -0.620189 | 0.338096 | 2.447870 | 1.593291 | -0.679146 | -0.034520 |
| 311 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 1.106955 | -0.270544 | 0.085049 | 0.0 | -0.996753 | -1.575686 | 1.530758 | 0.379672 | 0.845911 | -1.567907 | -0.274720 | 0.842528 | -0.678049 | 0.762698 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.635406 | -0.620189 | 0.338096 | 2.774431 | 1.317193 | 2.114728 | 1.927956 |
| 314 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0.227347 | -1.699389 | 0.099639 | -1.868426 | 0.0 | -0.990106 | 0.254625 | 1.629171 | 0.379672 | 1.749610 | -1.567907 | 2.244852 | -1.258980 | -0.678049 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.249709 | 0.155707 | 0.338096 | 2.284590 | 1.317193 | 2.735589 | 1.647603 |
| 326 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.323487 | -0.270544 | -0.891688 | 0.0 | -0.965182 | 0.254625 | -0.585122 | -1.026167 | 2.653309 | 1.153254 | 2.713155 | 0.959599 | -0.678049 | -0.057267 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.249709 | -0.620189 | 0.338096 | 2.284590 | 1.317193 | 3.356449 | -0.314873 |
| 390 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 0.445034 | 0.346427 | 0.085049 | 0.0 | -0.838898 | -1.575686 | -0.339090 | -1.026167 | 1.749610 | -0.660853 | 1.664360 | -1.400927 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.763972 | 0.155707 | 0.338096 | 2.937711 | 1.593291 | 0.252146 | 1.367249 |
| 400 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0.227347 | 1.030102 | -1.010909 | -1.868426 | 0.0 | -0.820620 | -0.660531 | -0.683535 | 0.379672 | 2.653309 | 0.246200 | 2.697219 | -0.857314 | -0.678049 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 0.155707 | 0.338096 | 2.284590 | 1.041095 | -0.368715 | 0.526188 |
| 411 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.526886 | -0.943263 | -0.270544 | 0.085049 | 0.0 | -0.790711 | -1.575686 | -1.224807 | 0.379672 | 2.653309 | -1.567907 | 2.775623 | -1.469932 | 0.923771 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.792496 | 1.707500 | -2.493820 | 3.590832 | 1.041095 | 2.735589 | 1.647603 |
| 417 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 1.476341 | -0.887515 | 1.061787 | 0.0 | -0.775756 | 0.254625 | 0.645041 | 0.379672 | 2.653309 | 0.246200 | 2.451593 | -0.181453 | -1.078504 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 2.121310 | 2.973780 | -0.368715 | 2.208310 |
| 425 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.735089 | 2.444129 | 1.061787 | 0.0 | -0.757478 | -0.660531 | 1.087899 | -1.026167 | 1.749610 | 0.246200 | 2.240177 | -0.702579 | -1.078504 | -0.057267 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.149668 | -0.620189 | 0.338096 | 3.264271 | 1.593291 | 3.977310 | 0.806541 |
| 427 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.526886 | 1.726730 | 2.320735 | 0.085049 | 0.0 | -0.750832 | 0.254625 | 0.694247 | -1.026167 | 0.845911 | -1.567907 | 0.799572 | -1.611738 | 0.523316 | 1.036019 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.378275 | 1.707500 | 1.754054 | 1.794749 | 2.421585 | 3.356449 | 1.927956 |
| 445 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | 0.779713 | 1.086793 | 2.038524 | 0.0 | -0.710953 | -1.575686 | 0.841867 | 0.379672 | 1.749610 | -0.660853 | 2.195344 | -0.624016 | 0.122861 | 2.129306 | 2.346151 | 1.191438 | 0.0 | -0.932014 | 3.306758 | -0.620189 | 0.338096 | 0.488508 | 1.317193 | 1.493867 | 0.806541 |
| 448 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | -1.240755 | -0.393938 | 0.085049 | 0.0 | -0.704306 | -0.660531 | 0.448214 | 0.379672 | 1.749610 | 0.246200 | 1.430846 | 0.850399 | 1.724681 | -0.057267 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.378275 | 0.155707 | 0.338096 | 2.121310 | 0.488900 | 0.873006 | 2.488664 |
| 466 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | 1.173890 | -0.887515 | 2.038524 | 0.0 | -0.664427 | -0.660531 | 1.235519 | 0.379672 | 1.749610 | -1.567907 | 2.144349 | -1.220894 | 1.724681 | 0.216054 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.378275 | -0.620189 | 0.338096 | 1.794749 | 3.249878 | 2.735589 | 1.086895 |
| 473 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.322365 | 1.097038 | 1.086793 | 1.061787 | 0.0 | -0.642826 | 1.169781 | -0.388296 | -1.026167 | 2.653309 | 0.246200 | 2.762025 | -1.712927 | -0.678049 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.535365 | 1.707500 | 0.338096 | 3.917392 | 1.317193 | -0.679146 | 1.367249 |
| 477 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 1.099517 | -0.764121 | 0.085049 | 0.0 | -0.632856 | -1.575686 | 1.629171 | 0.379672 | 2.653309 | -0.660853 | 2.485377 | -0.887390 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.663930 | -0.620189 | 0.338096 | 4.080672 | 0.212802 | 2.425158 | 0.806541 |
| 533 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0.336849 | -0.551565 | -0.517332 | 1.061787 | 0.0 | -0.491618 | 1.169781 | -0.880361 | -1.026167 | 0.845911 | -1.567907 | 0.843980 | 1.329362 | 0.923771 | 1.582663 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 1.121144 | -0.620189 | 0.338096 | 1.794749 | 2.421585 | -0.368715 | 2.208310 |
| 535 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -0.930867 | 0.099639 | 1.061787 | 0.0 | -0.488295 | -0.660531 | 0.349801 | -1.026167 | 2.653309 | 1.153254 | 2.685320 | -0.766244 | 0.122861 | -0.057267 | -0.426230 | -0.658973 | 0.0 | 2.589994 | 1.506840 | -0.620189 | -1.077862 | 2.284590 | 0.488900 | 3.046019 | 0.526188 |
| 538 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -1.211006 | -1.010909 | 0.085049 | 0.0 | -0.483310 | 1.169781 | -0.339090 | -1.026167 | 2.653309 | 0.246200 | 2.695519 | 0.737685 | -0.678049 | -0.877232 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | 2.447870 | 0.764998 | -0.058285 | 1.647603 |
| 544 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.103362 | -1.451479 | -0.764121 | 0.085049 | 0.0 | -0.463370 | 1.169781 | -0.831155 | 0.379672 | 1.749610 | 0.246200 | 1.544097 | -0.574546 | 2.525591 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 2.149668 | -0.620189 | -1.077862 | 2.447870 | -0.615492 | 2.735589 | 2.488664 |
| 561 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | -0.449922 | -0.764121 | 1.061787 | 0.0 | -0.413521 | 0.254625 | -1.716872 | -1.026167 | 1.749610 | -1.567907 | 2.199806 | -0.594362 | -0.678049 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 2.921061 | 0.155707 | 1.754054 | 4.407233 | 0.488900 | -0.368715 | 3.329725 |
| 584 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -0.576356 | -0.147150 | 0.085049 | 0.0 | -0.358688 | -0.660531 | 0.005356 | 0.379672 | 2.653309 | 1.153254 | 2.534247 | 0.268700 | -0.678049 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.635406 | 0.931603 | -1.077862 | 2.774431 | 0.764998 | 3.666880 | 1.367249 |
| 592 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.103362 | -1.488665 | -0.887515 | -0.891688 | 0.0 | -0.340410 | 0.254625 | -1.618459 | 0.379672 | 1.749610 | 1.153254 | 2.177708 | -0.187075 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.892537 | 0.155707 | -1.077862 | 3.100991 | 2.697683 | 0.252146 | -1.155935 |
| 595 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | -1.280421 | -0.887515 | 1.061787 | 0.0 | -0.332102 | 1.169781 | -1.716872 | 0.379672 | 2.653309 | -0.660853 | 2.707630 | 1.608898 | 1.724681 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 3.692454 | -0.620189 | 0.338096 | 3.917392 | 2.973780 | 3.356449 | 1.086895 |
| 616 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 1.278013 | 2.073946 | 1.061787 | 0.0 | -0.288899 | -1.575686 | 0.005356 | 0.379672 | 1.749610 | 0.246200 | 2.083155 | -1.225391 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.278234 | -0.620189 | -1.077862 | 2.121310 | 0.488900 | 0.562576 | 3.610079 |
| 635 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.210661 | -0.484629 | -0.023755 | 0.085049 | 0.0 | -0.240712 | 1.169781 | 0.005356 | -1.026167 | 0.845911 | 0.246200 | 0.888600 | 1.285092 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | 0.735447 | -0.620189 | 0.338096 | 1.631469 | 2.697683 | 0.873006 | 3.049371 |
| 649 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | -1.736576 | 1.703764 | 1.061787 | 0.0 | -0.205818 | 1.169781 | 0.300595 | 0.379672 | 1.749610 | 1.153254 | 1.651399 | 0.828193 | 1.324226 | 0.762698 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.792496 | -2.171982 | 0.338096 | 0.815068 | 1.317193 | 0.252146 | 1.086895 |
| 653 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.194645 | -0.887515 | 1.061787 | 0.0 | -0.199172 | -1.575686 | 1.579964 | 0.379672 | 1.749610 | -1.567907 | 2.426733 | -1.372959 | -0.678049 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.535365 | 0.155707 | 0.338096 | 3.917392 | 0.488900 | 3.666880 | 0.806541 |
| 677 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -0.682957 | -0.147150 | -0.891688 | 0.0 | -0.134368 | -1.575686 | -0.732742 | 0.379672 | 0.845911 | -0.660853 | 0.191245 | 1.147362 | 0.523316 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.278234 | 0.155707 | -1.077862 | 3.100991 | 1.317193 | -0.368715 | 0.806541 |
| 681 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.117845 | 1.315199 | -1.010909 | 0.085049 | 0.0 | -0.124398 | 1.169781 | 0.694247 | 0.379672 | 0.845911 | -1.567907 | 1.504151 | 0.278116 | -0.678049 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.478316 | 0.155707 | 0.338096 | 1.304909 | 2.145487 | 0.873006 | 1.927956 |
| 699 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | 0.621051 | -1.010909 | -0.891688 | 0.0 | -0.081196 | 1.169781 | 0.202182 | 0.379672 | 1.749610 | 1.153254 | 2.251438 | -0.068036 | -0.277594 | -0.057267 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.892537 | -0.620189 | -1.077862 | 0.325228 | 1.041095 | 1.493867 | 1.086895 |
| 701 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 1.421801 | -0.887515 | -0.891688 | 0.0 | -0.072888 | 0.254625 | -1.027981 | 0.379672 | 1.749610 | 0.246200 | 1.773999 | -0.052717 | 1.324226 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 1.754054 | 1.631469 | 2.421585 | 3.977310 | -0.595227 |
| 716 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | 0.093002 | -0.023755 | 0.085049 | 0.0 | -0.042979 | -1.575686 | -0.093057 | 0.379672 | 2.653309 | 0.246200 | 2.744389 | -1.486657 | -0.277594 | 0.489376 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.249709 | -0.620189 | 1.754054 | 1.794749 | 3.249878 | -0.679146 | 1.927956 |
| 736 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | 1.369739 | -0.640727 | 1.061787 | 0.0 | -0.001438 | 0.254625 | 0.595834 | -1.026167 | 0.845911 | 0.246200 | 0.955319 | 1.114756 | 1.724681 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 2.021103 | 0.155707 | 0.338096 | 1.304909 | 1.869389 | 0.562576 | 1.086895 |
| 738 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.834182 | -1.010909 | -1.868426 | 0.0 | 0.001885 | 1.169781 | -0.043851 | -1.026167 | 1.749610 | 1.153254 | 1.325669 | -1.019358 | -0.678049 | 0.216054 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 0.155707 | 0.338096 | 2.284590 | 0.488900 | 2.735589 | 1.086895 |
| 746 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -1.377106 | -0.270544 | -1.868426 | 0.0 | 0.016840 | -0.660531 | -0.535916 | -2.432006 | 2.653309 | 0.246200 | 2.862102 | 0.839155 | -0.678049 | 1.855984 | 2.346151 | -0.658973 | 0.0 | 1.415991 | 1.249709 | 0.155707 | 0.338096 | 2.284590 | 3.249878 | 0.873006 | 1.647603 |
| 749 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | -1.330003 | -0.887515 | -1.868426 | 0.0 | 0.021825 | -1.575686 | -0.437503 | -2.432006 | 2.653309 | 1.153254 | 2.834905 | 1.620844 | -0.678049 | -0.057267 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.792496 | 0.155707 | 0.338096 | 4.080672 | 2.697683 | 1.183437 | 1.367249 |
| 750 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.774856 | 1.600296 | 2.320735 | 0.085049 | 0.0 | 0.023487 | 1.169781 | -0.634329 | 1.785511 | 1.749610 | 1.153254 | 1.448482 | -0.362048 | 0.122861 | 0.762698 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.506840 | -0.620189 | 0.338096 | 0.815068 | 1.869389 | 2.735589 | 1.927956 |
| 760 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | -1.682035 | -0.887515 | 0.085049 | 0.0 | 0.041765 | 0.254625 | -1.372427 | -1.026167 | 0.845911 | -0.660853 | 0.217168 | 1.296335 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.406799 | -0.620189 | 0.338096 | 1.304909 | 0.764998 | 1.183437 | 2.208310 |
| 799 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -0.826745 | -0.887515 | -0.891688 | 0.0 | 0.139801 | 1.169781 | -1.520046 | 0.379672 | 1.749610 | -1.567907 | 2.371701 | 0.012072 | -1.078504 | 0.489376 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 0.155707 | 0.338096 | 2.447870 | 0.488900 | 3.356449 | 0.806541 |
| 810 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.982929 | -0.764121 | -1.868426 | 0.0 | 0.164725 | -1.575686 | -0.683535 | 0.379672 | 1.749610 | 0.246200 | 2.329206 | 0.180299 | 0.122861 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 0.155707 | 0.338096 | 0.815068 | 1.317193 | 0.562576 | 1.367249 |
| 813 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -1.486186 | -0.887515 | 0.085049 | 0.0 | 0.169710 | -1.575686 | 0.891073 | 0.379672 | 1.749610 | 1.153254 | 1.203919 | -0.107669 | 1.724681 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 2.589994 | 1.249709 | 0.931603 | 0.338096 | 1.794749 | 0.764998 | 2.735589 | 0.245834 |
| 814 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 1.253222 | 0.593216 | 0.085049 | 0.0 | 0.171371 | 0.254625 | -1.077188 | -1.026167 | 2.653309 | 0.246200 | 2.788372 | 0.454073 | -0.678049 | -0.330589 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 1.754054 | 2.121310 | 0.764998 | 0.562576 | 1.367249 |
| 837 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | -0.519337 | -0.023755 | 1.061787 | 0.0 | 0.234513 | -0.660531 | 1.087899 | 0.379672 | 0.845911 | 0.246200 | 1.486515 | -0.074642 | 2.525591 | 0.489376 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.121144 | 0.155707 | -1.077862 | 1.794749 | 0.764998 | -0.058285 | 2.488664 |
| 838 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.555852 | -0.796996 | 0.346427 | 0.085049 | 0.0 | 0.236175 | 0.254625 | -1.077188 | 0.379672 | 1.749610 | -1.567907 | 1.541547 | -1.667673 | -1.078504 | -0.877232 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.378275 | -0.620189 | -1.077862 | 2.284590 | 1.317193 | 3.356449 | 2.769018 |
| 858 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 1.042498 | -0.270544 | -0.891688 | 0.0 | 0.292670 | 1.169781 | -0.781948 | 0.379672 | 2.653309 | 0.246200 | 2.571644 | 0.608106 | 0.122861 | 0.762698 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.892537 | 2.483396 | 0.338096 | -0.001333 | 0.764998 | 0.562576 | 0.806541 |
| 861 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 1.486257 | -0.887515 | 0.085049 | 0.0 | 0.297655 | 0.254625 | 0.152975 | 0.379672 | 1.749610 | -1.567907 | 2.240602 | 1.375038 | 2.125136 | 2.129306 | 2.346151 | -1.584178 | 0.0 | -0.932014 | 2.149668 | -0.620189 | 0.338096 | 3.100991 | 2.973780 | 3.977310 | 1.367249 |
| 875 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | -1.493624 | 2.444129 | 1.061787 | 0.0 | 0.332549 | 1.169781 | -1.667666 | 0.379672 | -0.057788 | 1.153254 | -0.416869 | -0.923228 | -0.678049 | 2.675949 | 2.346151 | -0.658973 | 0.0 | -0.932014 | 1.121144 | 0.155707 | 0.338096 | 2.121310 | 1.869389 | 3.356449 | 3.610079 |
| 894 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | -0.291259 | -0.764121 | 0.085049 | 0.0 | 0.374090 | 1.169781 | 0.940280 | 0.379672 | 1.749610 | 1.153254 | 2.395924 | 1.287481 | 0.122861 | -0.330589 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 3.178192 | -0.620189 | 0.338096 | 0.488508 | 1.317193 | -0.679146 | 1.367249 |
| 907 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | 0.735089 | -0.517332 | 0.085049 | 0.0 | 0.402338 | -0.660531 | 1.087899 | 0.379672 | 2.653309 | -0.660853 | 2.488140 | -0.781703 | 1.724681 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.892537 | 1.707500 | 0.338096 | 2.447870 | 1.317193 | 0.252146 | 1.647603 |
| 913 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 0.884358 | 1.602775 | -0.887515 | 0.085049 | 0.0 | 0.418954 | -1.575686 | 1.383138 | -2.432006 | 2.653309 | -0.660853 | 2.617964 | -1.661208 | -0.277594 | 0.216054 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.892537 | -0.620189 | 0.338096 | 2.774431 | 1.593291 | -0.368715 | 1.927956 |
| 914 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -1.550643 | -0.147150 | -1.868426 | 0.0 | 0.420616 | 1.169781 | -1.421633 | -1.026167 | 1.749610 | -0.660853 | 1.503089 | 1.585147 | -0.678049 | -0.057267 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.921061 | 0.155707 | 0.338096 | 4.243953 | 1.317193 | 3.977310 | -1.155935 |
| 918 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.541369 | -1.401897 | -0.023755 | 0.085049 | 0.0 | 0.427262 | 1.169781 | 0.841867 | 0.379672 | 2.653309 | -0.660853 | 2.835330 | 0.686247 | 0.523316 | 2.402628 | 2.346151 | -1.584178 | 0.0 | 0.241988 | 2.535365 | 1.707500 | -1.077862 | 3.590832 | 1.593291 | 2.735589 | 1.647603 |
| 922 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | 0.982999 | -0.640727 | -0.891688 | 0.0 | 0.437232 | 0.254625 | 1.284725 | 1.785511 | 2.653309 | -1.567907 | 2.695731 | 0.444657 | -0.678049 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 1.892537 | 0.931603 | -1.077862 | 2.937711 | 1.317193 | 3.666880 | 2.488664 |
| 926 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | -0.673041 | -0.640727 | 1.061787 | 0.0 | 0.445540 | 1.169781 | -0.486709 | -1.026167 | 0.845911 | 1.153254 | 0.792135 | 0.850399 | 0.122861 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.506840 | 0.155707 | 1.754054 | 2.284590 | 0.764998 | 3.977310 | 3.610079 |
| 937 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0.227347 | -0.968054 | 0.469821 | 1.061787 | 0.0 | 0.468803 | 0.254625 | 1.383138 | -1.026167 | 1.749610 | -0.660853 | 2.256538 | 0.424560 | 1.324226 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 1.249709 | 0.931603 | 0.338096 | 1.958030 | 1.317193 | 3.977310 | -0.595227 |
| 954 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.555852 | -0.762289 | -0.887515 | -1.868426 | 0.0 | 0.513667 | 0.254625 | -1.421633 | 0.379672 | 1.749610 | 0.246200 | 2.413347 | 1.724282 | -1.078504 | -0.603911 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.249709 | 0.155707 | -1.077862 | 2.121310 | 1.041095 | -0.058285 | 1.647603 |
| 955 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -1.290337 | -0.887515 | -0.891688 | 0.0 | 0.516990 | 1.169781 | -0.388296 | -2.432006 | 2.653309 | 0.246200 | 2.695094 | -1.028915 | 0.523316 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 1.707500 | 0.338096 | 1.958030 | 1.317193 | 2.114728 | 1.927956 |
| 962 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.541369 | -0.080535 | -0.517332 | 0.085049 | 0.0 | 0.543576 | 0.254625 | 0.891073 | 0.379672 | 1.749610 | -0.660853 | 1.598491 | 0.460257 | -0.678049 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.792496 | -0.620189 | 0.338096 | 4.243953 | 1.317193 | -0.679146 | 1.647603 |
| 975 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -1.327524 | 0.469821 | 1.061787 | 0.0 | 0.576809 | -1.575686 | 0.940280 | 1.785511 | 1.749610 | 0.246200 | 1.528161 | -0.707779 | 1.324226 | 0.489376 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.635406 | -0.620189 | -1.077862 | 1.958030 | 0.764998 | 0.252146 | 1.086895 |
| 976 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.088878 | 1.404447 | 1.703764 | 0.085049 | 0.0 | 0.578470 | 1.169781 | 0.103769 | 0.379672 | 1.749610 | -0.660853 | 1.465905 | 0.551187 | 0.523316 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.792496 | -2.171982 | 0.338096 | 1.958030 | 3.249878 | 3.977310 | 1.367249 |
| 999 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | 0.854086 | 0.099639 | 0.085049 | 0.0 | 0.636627 | 0.254625 | -1.716872 | 0.379672 | 1.749610 | -1.567907 | 2.187695 | 0.323651 | -1.078504 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 1.707500 | 0.338096 | 2.121310 | 0.764998 | -0.679146 | 1.367249 |
| 1008 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.417764 | -1.010909 | 0.085049 | 0.0 | 0.659890 | 1.169781 | -0.585122 | 0.379672 | 1.749610 | 1.153254 | 2.300096 | -1.217240 | 1.324226 | 1.036019 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.278234 | 0.155707 | -1.077862 | 2.121310 | 0.764998 | 3.046019 | 0.806541 |
| 1009 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | 0.626009 | -1.010909 | 0.085049 | 0.0 | 0.661552 | 1.169781 | 0.497421 | 0.379672 | 2.653309 | -1.567907 | 2.804308 | 1.144410 | 0.122861 | 1.582663 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | 0.338096 | 0.325228 | 1.041095 | -0.368715 | 0.245834 |
| 1024 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | -1.099446 | -0.887515 | 1.061787 | 0.0 | 0.694784 | -1.575686 | 0.792660 | 0.379672 | 1.749610 | 0.246200 | 2.266312 | 1.741288 | 0.122861 | 1.036019 | -0.426230 | -0.658973 | 0.0 | 1.415991 | 1.892537 | -0.620189 | 1.754054 | 2.121310 | 3.525976 | 0.873006 | 0.526188 |
| 1043 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | -0.881285 | -0.887515 | 0.085049 | 0.0 | 0.742971 | 1.169781 | -1.323220 | 1.785511 | 1.749610 | -0.660853 | 2.144987 | 0.766074 | 0.523316 | -0.877232 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 3.049627 | -0.620189 | -1.077862 | 0.325228 | 1.041095 | 1.804297 | 1.086895 |
| 1055 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | 0.065732 | 0.716610 | 0.085049 | 0.0 | 0.764573 | -0.660531 | 0.251388 | 0.379672 | 1.749610 | -1.567907 | 2.231890 | -0.335064 | 1.724681 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 0.606882 | 0.155707 | -1.077862 | 1.141629 | 1.041095 | 1.183437 | 1.367249 |
| 1076 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.613613 | 0.223033 | 1.061787 | 0.0 | 0.822730 | -0.660531 | 1.038693 | 0.379672 | 1.749610 | 1.153254 | 2.024724 | 1.425492 | 0.122861 | 1.309341 | 2.346151 | -1.584178 | 0.0 | 0.241988 | 1.892537 | -0.620189 | 0.338096 | 1.141629 | 1.317193 | -0.368715 | 2.208310 |
| 1078 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | -1.652286 | 2.320735 | 0.085049 | 0.0 | 0.827714 | 1.169781 | -1.667666 | 0.379672 | 1.749610 | -1.567907 | 2.087617 | 1.090724 | 0.122861 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.635406 | -1.396086 | 1.754054 | 2.121310 | 0.488900 | 3.666880 | 3.610079 |
| 1086 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | -1.163903 | 1.580370 | 2.038524 | 0.0 | 0.854300 | 0.254625 | 1.087899 | -2.432006 | 1.749610 | 1.153254 | 1.680296 | 1.424649 | -0.678049 | -0.603911 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.663930 | -0.620189 | 0.338096 | 4.080672 | 0.488900 | 3.356449 | 1.367249 |
| 1093 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.555852 | -1.275463 | -0.887515 | 0.085049 | 0.0 | 0.867593 | 1.169781 | -1.274014 | 0.379672 | 0.845911 | 1.153254 | 0.769400 | 0.604030 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.635406 | 0.155707 | -2.493820 | 2.121310 | 1.041095 | 3.356449 | 1.367249 |
| 1096 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.336849 | 0.236790 | -0.393938 | -0.891688 | 0.0 | 0.872578 | 0.254625 | -1.372427 | 0.379672 | 1.749610 | 1.153254 | 2.110778 | 0.431165 | -0.678049 | 1.582663 | 2.346151 | 1.191438 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 2.284590 | 0.764998 | 1.493867 | 0.806541 |
| 1111 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | -0.484629 | -0.887515 | 2.038524 | 0.0 | 0.909134 | 0.254625 | 0.595834 | -1.026167 | 0.845911 | 1.153254 | 0.778961 | 0.042850 | -1.078504 | 0.216054 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.921061 | 0.931603 | 0.338096 | 4.243953 | 0.764998 | -0.368715 | 1.367249 |
| 1116 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -0.291259 | 2.073946 | 2.038524 | 0.0 | 0.919104 | 0.254625 | -0.289883 | -1.026167 | 2.653309 | 1.153254 | 2.779873 | 1.226065 | -0.678049 | 1.582663 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 3.178192 | 0.155707 | 0.338096 | 4.733793 | 0.488900 | -0.058285 | 2.488664 |
| 1135 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.593710 | -1.010909 | 1.061787 | 0.0 | 0.958983 | 1.169781 | -0.486709 | 1.785511 | 1.749610 | -1.567907 | 2.350878 | -1.568030 | -0.678049 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 2.021103 | 1.707500 | -2.493820 | 3.100991 | -1.167687 | -0.679146 | 2.208310 |
| 1138 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 1.069768 | 1.333581 | 2.038524 | 0.0 | 0.965630 | -0.660531 | -1.224807 | 0.379672 | 1.749610 | 0.246200 | 1.007588 | 0.896074 | -0.277594 | -0.057267 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | 0.338096 | 3.754112 | 1.041095 | 3.046019 | 2.488664 |
| 1140 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.774856 | 1.265617 | -0.270544 | 0.085049 | 0.0 | 0.968953 | -0.660531 | -1.716872 | 0.379672 | 2.653309 | 1.153254 | 2.665772 | -1.512797 | -1.078504 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 0.931603 | -1.077862 | 2.447870 | 0.764998 | -0.368715 | 1.647603 |
| 1156 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 0.202082 | 0.716610 | 0.085049 | 0.0 | 1.002185 | -1.575686 | 0.694247 | -1.026167 | 0.845911 | 0.246200 | 0.835481 | 1.614379 | -0.678049 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 0.864013 | -0.620189 | 0.338096 | 1.794749 | 2.973780 | 3.666880 | 2.208310 |
| 1177 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.026066 | 0.963398 | 2.038524 | 0.0 | 1.048711 | 1.169781 | -0.781948 | -1.026167 | 0.845911 | -1.567907 | 1.437645 | 1.077653 | 0.923771 | -0.057267 | -0.426230 | 0.266233 | 0.0 | 2.589994 | 0.992578 | 0.155707 | 0.338096 | 1.141629 | 1.869389 | -0.368715 | 1.927956 |
| 1181 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.322365 | -0.836662 | -0.393938 | -1.868426 | 0.0 | 1.057019 | 0.254625 | -1.224807 | -1.026167 | 1.749610 | 0.246200 | 1.585743 | -0.373994 | -0.277594 | 1.036019 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 2.406799 | 0.155707 | 0.338096 | 1.304909 | 1.869389 | -0.058285 | 2.208310 |
| 1184 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | -0.541648 | 1.580370 | 2.038524 | 0.0 | 1.063666 | -0.660531 | 1.235519 | 0.379672 | 1.749610 | 0.246200 | 2.320919 | 0.614430 | 0.122861 | 2.675949 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 3.178192 | 2.483396 | 0.338096 | 0.488508 | 1.041095 | 0.562576 | 0.806541 |
| 1185 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 1.211077 | 0.716610 | -0.891688 | 0.0 | 1.065327 | 0.254625 | -0.043851 | -1.026167 | 1.749610 | 0.246200 | 2.358528 | -1.516170 | -0.678049 | 2.402628 | 2.346151 | -1.584178 | 0.0 | 0.241988 | 0.349751 | 0.155707 | 0.338096 | 1.141629 | 1.593291 | 1.183437 | 1.927956 |
| 1221 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 1.079684 | -1.010909 | -1.868426 | 0.0 | 1.141762 | 0.254625 | 0.399008 | -1.026167 | 0.845911 | 0.246200 | 0.901986 | -1.534440 | 0.122861 | 2.129306 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 1.763972 | 0.155707 | -1.077862 | 2.611150 | 2.973780 | 3.666880 | -0.034520 |
| 1223 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 0.720215 | -0.023755 | 0.085049 | 0.0 | 1.148409 | 0.254625 | 0.792660 | -2.432006 | 1.749610 | 0.246200 | 1.366890 | 1.384454 | 1.724681 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.763972 | 0.155707 | -2.493820 | 2.611150 | 0.212802 | 3.666880 | 1.647603 |
| 1225 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 0.502054 | 2.320735 | -0.891688 | 0.0 | 1.153393 | 1.169781 | -0.880361 | -1.026167 | 1.749610 | -0.660853 | 2.167509 | 0.394343 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 2.284590 | 0.488900 | 1.804297 | 0.526188 |
| 1242 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | -0.474713 | -0.270544 | 1.061787 | 0.0 | 1.188288 | -0.660531 | 1.087899 | 0.379672 | 2.653309 | -0.660853 | 2.832355 | -1.400364 | -0.678049 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.249709 | 0.155707 | -1.077862 | 2.284590 | 1.041095 | 3.046019 | 1.086895 |
| 1295 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.446350 | -0.016079 | -0.640727 | -1.868426 | 0.0 | 1.312910 | 0.254625 | 0.743454 | 0.379672 | 0.845911 | 0.246200 | 0.838030 | 1.706855 | -1.078504 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | 0.155707 | 1.754054 | 2.447870 | 2.697683 | 3.356449 | 0.245834 |
| 1301 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.307882 | -1.121758 | -0.887515 | 0.085049 | 0.0 | 1.327864 | -0.660531 | -0.683535 | 0.379672 | 1.749610 | -0.660853 | 2.079756 | 1.161416 | 0.523316 | 1.855984 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 3.306758 | -2.171982 | -1.077862 | 1.468189 | 1.317193 | 3.666880 | 2.769018 |
| 1303 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.103362 | 0.492137 | -0.640727 | 0.085049 | 0.0 | 1.332849 | 0.254625 | 1.284725 | -1.026167 | 0.845911 | -0.660853 | 0.813808 | 0.696787 | 2.125136 | -0.877232 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.149668 | 0.931603 | 0.338096 | 2.447870 | 1.869389 | 3.666880 | 1.647603 |
| 1327 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 1.280492 | -0.764121 | 0.085049 | 0.0 | 1.392668 | -1.575686 | -1.027981 | 1.785511 | 1.749610 | -1.567907 | 1.428296 | -0.923931 | -0.277594 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.763972 | 1.707500 | 0.338096 | 1.958030 | 3.525976 | -0.058285 | 1.086895 |
| 1330 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 0.050857 | -0.393938 | 0.085049 | 0.0 | 1.397653 | -1.575686 | 0.743454 | -1.026167 | 2.653309 | 0.246200 | 2.738652 | 1.156075 | 1.724681 | -0.603911 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.249709 | -0.620189 | 0.338096 | 1.468189 | 2.145487 | 1.183437 | 2.769018 |
| 1331 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | 1.044977 | 0.099639 | 0.085049 | 0.0 | 1.399314 | 1.169781 | 1.235519 | -1.026167 | 2.653309 | -0.660853 | 2.796659 | -0.102609 | 0.523316 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.278234 | 0.155707 | 0.338096 | 2.447870 | 1.593291 | 3.046019 | 1.367249 |
| 1348 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.336849 | 0.829295 | -1.010909 | 1.061787 | 0.0 | 1.440855 | -1.575686 | 1.579964 | 0.379672 | 1.749610 | -1.567907 | 2.192794 | 0.657436 | -0.277594 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | 1.958030 | 0.764998 | 2.735589 | 3.329725 |
| 1351 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | -1.699389 | 1.580370 | 0.085049 | 0.0 | 1.454148 | 1.169781 | -0.388296 | 0.379672 | 1.749610 | 1.153254 | 2.267374 | -1.669079 | 0.122861 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.635406 | 0.155707 | 0.338096 | 2.447870 | 3.525976 | 0.562576 | 0.806541 |
| 1403 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -1.694431 | 0.716610 | 1.061787 | 0.0 | 1.578770 | -0.660531 | 0.546627 | 0.379672 | 1.749610 | -1.567907 | 1.452944 | 1.515719 | -1.078504 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.249709 | 0.155707 | 0.338096 | 2.121310 | 1.041095 | 2.735589 | 1.647603 |
| 1414 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 0.935897 | 1.950552 | 0.085049 | 0.0 | 1.608679 | -1.575686 | 0.891073 | 0.379672 | 0.845911 | 0.246200 | 0.452594 | -0.172739 | -0.277594 | 2.129306 | 2.346151 | -0.658973 | 0.0 | -0.932014 | 1.763972 | 0.155707 | 0.338096 | 1.631469 | 2.697683 | 3.046019 | 1.927956 |
| 1430 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.117845 | -1.491145 | 0.099639 | 0.085049 | 0.0 | 1.645235 | -0.660531 | 1.629171 | -2.432006 | 0.845911 | 0.246200 | 1.424259 | -1.537111 | 0.122861 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.121144 | 0.155707 | 0.338096 | 1.794749 | 3.249878 | -0.368715 | 1.927956 |
| 1443 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -1.245713 | -0.887515 | 0.085049 | 0.0 | 1.671821 | -1.575686 | -0.486709 | 0.379672 | 2.653309 | 0.246200 | 2.629863 | 0.421468 | 0.923771 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.635406 | -0.620189 | -1.077862 | 2.447870 | 0.488900 | 0.562576 | 2.769018 |
| 1445 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | -0.546607 | 2.320735 | 1.061787 | 0.0 | 1.676806 | -1.575686 | -0.289883 | -1.026167 | 1.749610 | -0.660853 | 1.501601 | -1.218926 | -1.078504 | 2.129306 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 0.155707 | 0.338096 | 2.121310 | 0.764998 | -0.679146 | 1.647603 |
| 1462 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -0.199532 | 1.827158 | -1.868426 | 0.0 | 1.713362 | -0.660531 | -0.289883 | -1.026167 | 1.749610 | 1.153254 | 1.174597 | -0.770882 | -1.078504 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.249709 | -0.620189 | -1.077862 | 2.121310 | 1.317193 | 2.114728 | 0.526188 |
index_3 = np.where(y_means==3)
index_3
(array([ 29, 50, 65, 67, 70, 77, 82, 85, 95, 105, 106,
112, 131, 136, 147, 163, 165, 189, 209, 213, 233, 279,
280, 290, 316, 329, 348, 367, 375, 376, 379, 392, 401,
406, 408, 424, 429, 455, 465, 489, 492, 497, 510, 534,
536, 541, 552, 568, 575, 588, 609, 624, 625, 627, 646,
674, 706, 714, 721, 723, 728, 741, 743, 745, 755, 758,
766, 770, 771, 774, 779, 787, 789, 804, 806, 812, 821,
851, 867, 869, 887, 890, 898, 899, 904, 916, 919, 936,
945, 947, 956, 966, 971, 987, 994, 1010, 1031, 1034, 1054,
1080, 1103, 1126, 1129, 1154, 1166, 1176, 1194, 1195, 1209, 1231,
1235, 1264, 1268, 1275, 1277, 1278, 1305, 1310, 1357, 1370, 1374,
1377, 1396, 1401, 1437, 1461], dtype=int64),)
Group_3 = x.iloc[index_3]
Group_3
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 29 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.241677 | -0.887515 | 1.061787 | 0.0 | -1.639803 | -0.660531 | 0.841867 | 0.379672 | 2.653309 | -1.567907 | 2.644099 | 1.195848 | 0.122861 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.378275 | -0.620189 | -1.077862 | -0.817734 | -0.615492 | -0.058285 | -0.875581 |
| 50 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.212863 | -0.437526 | -1.010909 | -0.891688 | 0.0 | -1.596600 | -1.575686 | 1.579964 | -1.026167 | 0.845911 | 0.246200 | -0.238386 | 0.700020 | 2.525591 | -0.603911 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.506840 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 65 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | 0.083085 | -0.147150 | 0.085049 | 0.0 | -1.563368 | 1.169781 | -1.618459 | 0.379672 | 1.749610 | 0.246200 | 1.753601 | 0.761296 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 2.589994 | 1.249709 | -0.620189 | 0.338096 | -0.327893 | -1.167687 | -0.679146 | -0.595227 |
| 67 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 1.330074 | -0.270544 | 0.085049 | 0.0 | -1.560045 | -0.660531 | -0.339090 | 0.379672 | 0.845911 | -1.567907 | 0.684408 | 0.628766 | -0.277594 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.763972 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 70 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.417384 | 1.047456 | -1.010909 | -1.868426 | 0.0 | -1.551736 | -1.575686 | -0.437503 | -1.026167 | -0.057788 | 0.246200 | -0.218838 | 1.455287 | 1.724681 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.121144 | -0.620189 | -1.077862 | -0.491174 | -0.339394 | -0.368715 | -0.314873 |
| 77 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.884358 | -1.510977 | -0.393938 | 1.061787 | 0.0 | -1.535120 | 1.169781 | -0.683535 | 0.379672 | 0.845911 | -1.567907 | 1.432546 | 0.105953 | 0.523316 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 0.735447 | 0.155707 | 1.754054 | -1.144294 | -1.167687 | -0.679146 | -1.155935 |
| 82 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -1.714264 | -1.010909 | -0.891688 | 0.0 | -1.526812 | -1.575686 | 0.202182 | 0.379672 | 0.845911 | 1.153254 | 0.793835 | 0.531090 | 0.122861 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.635406 | 0.931603 | 0.338096 | -0.981014 | -1.167687 | -0.368715 | -1.155935 |
| 85 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | 1.481299 | -0.270544 | 0.085049 | 0.0 | -1.516842 | 1.169781 | -0.831155 | -2.432006 | 0.845911 | 1.153254 | 0.160861 | 1.037880 | 0.523316 | -1.150554 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 3.306758 | 0.155707 | -1.077862 | -0.164613 | -0.063296 | -0.679146 | -0.595227 |
| 95 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 1.027623 | -0.887515 | 1.061787 | 0.0 | -1.493579 | -1.575686 | -0.289883 | 0.379672 | 0.845911 | 0.246200 | 1.497139 | 1.361546 | 2.525591 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 0.606882 | 1.707500 | -2.493820 | -0.491174 | -0.339394 | -0.679146 | -0.314873 |
| 105 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.417384 | 1.530881 | -0.887515 | 1.061787 | 0.0 | -1.470317 | 0.254625 | -1.667666 | -1.026167 | 2.653309 | 1.153254 | 2.622214 | 1.069361 | 2.525591 | 1.582663 | 2.346151 | 1.191438 | 0.0 | 0.241988 | 2.406799 | 0.155707 | 0.338096 | -0.654454 | -0.615492 | -0.058285 | -0.595227 |
| 106 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 0.774755 | -1.010909 | 0.085049 | 0.0 | -1.468655 | -1.575686 | 0.349801 | 0.379672 | 2.653309 | -0.660853 | 2.479428 | -0.640600 | 0.122861 | 1.036019 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 2.149668 | -1.396086 | -1.077862 | 0.161947 | -0.339394 | -0.679146 | 0.806541 |
| 112 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.869874 | -1.637412 | 2.073946 | 0.085049 | 0.0 | -1.457024 | 1.169781 | -1.766079 | 1.785511 | 1.749610 | 1.153254 | 2.300096 | -0.062134 | -0.277594 | -0.877232 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.506840 | 0.155707 | 0.338096 | -0.327893 | -0.339394 | 0.562576 | -0.034520 |
| 131 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.016150 | 0.346427 | 0.085049 | 0.0 | -1.413821 | 0.254625 | 0.546627 | 0.379672 | 0.845911 | 1.153254 | 0.574769 | -1.077682 | 0.523316 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.606882 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.875581 |
| 136 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 0.861523 | -0.147150 | 1.061787 | 0.0 | -1.405513 | -1.575686 | -0.634329 | -2.432006 | 0.845911 | 1.153254 | 0.881164 | 1.523028 | -0.277594 | -0.057267 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.864013 | -0.620189 | 0.338096 | -0.491174 | -0.615492 | -0.679146 | -0.314873 |
| 147 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.446350 | 0.135146 | 0.099639 | 0.085049 | 0.0 | -1.372281 | 1.169781 | 1.235519 | -1.026167 | 1.749610 | -1.567907 | 2.268862 | -0.200285 | 0.523316 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.249709 | -0.620189 | -1.077862 | -0.001333 | 0.488900 | 1.493867 | 0.806541 |
| 163 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.198380 | -1.161424 | 1.827158 | -0.891688 | 0.0 | -1.332402 | 0.254625 | 0.841867 | 1.785511 | 0.845911 | 1.153254 | 0.623852 | 1.277362 | 0.122861 | 0.216054 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.092620 | -0.620189 | -2.493820 | -0.327893 | -0.339394 | -0.368715 | -0.034520 |
| 165 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 1.610212 | 0.223033 | 0.085049 | 0.0 | -1.327417 | 0.254625 | -0.634329 | 0.379672 | 2.653309 | -0.660853 | 2.852116 | 0.385068 | 0.122861 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.249709 | 1.707500 | 0.338096 | -0.327893 | -0.063296 | 0.562576 | -0.034520 |
| 189 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | -1.213485 | -0.764121 | 0.085049 | 0.0 | -1.274245 | 1.169781 | 1.579964 | 0.379672 | 1.749610 | -0.660853 | 1.536448 | -1.000807 | 0.122861 | 0.762698 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.249709 | 2.483396 | 0.338096 | -0.001333 | 0.764998 | -0.368715 | -1.155935 |
| 209 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.392902 | -1.010909 | 1.061787 | 0.0 | -1.224396 | 1.169781 | 1.530758 | 0.379672 | 0.845911 | -1.567907 | 0.614715 | -0.273367 | 1.724681 | 0.216054 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.735447 | 0.155707 | 0.338096 | -0.491174 | -0.615492 | -0.679146 | -0.314873 |
| 213 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 1.652357 | -0.147150 | 1.061787 | 0.0 | -1.211103 | -0.660531 | 0.743454 | -1.026167 | 0.845911 | -0.660853 | 1.272124 | 0.199975 | 0.923771 | 0.216054 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 0.606882 | 1.707500 | -2.493820 | 0.488508 | 1.317193 | 0.562576 | 0.806541 |
| 233 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.127709 | -1.010909 | 1.061787 | 0.0 | -1.166239 | 1.169781 | 0.103769 | 0.379672 | 2.653309 | 1.153254 | 2.765212 | 1.377989 | 0.122861 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | -1.077862 | -0.001333 | -1.167687 | -0.679146 | 0.526188 |
| 279 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | -0.013600 | -0.640727 | -1.868426 | 0.0 | -1.063218 | -1.575686 | 1.481551 | 0.379672 | 2.653309 | -0.660853 | 2.685957 | 0.211078 | 0.122861 | -0.330589 | -0.426230 | -1.584178 | 0.0 | 1.415991 | 2.149668 | 0.931603 | -1.077862 | 0.488508 | -0.063296 | -0.368715 | 0.526188 |
| 280 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.760373 | 0.663195 | -0.764121 | 1.061787 | 0.0 | -1.061556 | 0.254625 | -1.027981 | 0.379672 | 1.749610 | 0.246200 | 2.354491 | 0.942031 | 0.122861 | 0.216054 | -0.426230 | 1.191438 | 0.0 | 2.589994 | 1.249709 | 1.707500 | -1.077862 | -0.327893 | -0.339394 | -0.368715 | -0.314873 |
| 290 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.322365 | -0.412735 | 0.099639 | 1.061787 | 0.0 | -1.044940 | 0.254625 | -1.520046 | 0.379672 | 2.653309 | -1.567907 | 2.584180 | 1.585428 | 2.525591 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.378275 | 0.931603 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.595227 |
| 316 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.322365 | 0.715256 | -1.010909 | -0.891688 | 0.0 | -0.986783 | 0.254625 | 1.186312 | -1.026167 | 1.749610 | 0.246200 | 1.585318 | 0.491457 | 1.724681 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.763972 | -0.620189 | 0.338096 | -0.001333 | -0.891589 | -0.679146 | 0.806541 |
| 329 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 1.684585 | -0.517332 | 2.038524 | 0.0 | -0.960197 | 1.169781 | -1.175601 | 0.379672 | 2.653309 | 0.246200 | 2.506625 | 0.289781 | 0.523316 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.249709 | -0.620189 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.875581 |
| 348 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | 0.018629 | 1.703764 | 2.038524 | 0.0 | -0.925303 | -1.575686 | -1.077188 | 0.379672 | 1.749610 | 1.153254 | 2.016225 | 0.223164 | -0.277594 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 0.606882 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 367 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.603697 | 0.099639 | 0.085049 | 0.0 | -0.887085 | 1.169781 | 1.678377 | -1.026167 | 0.845911 | 1.153254 | 0.848442 | -1.624387 | 1.324226 | -0.057267 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.121144 | -0.620189 | 0.338096 | -0.491174 | -0.339394 | -0.368715 | -0.314873 |
| 375 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 1.136704 | -0.270544 | 0.085049 | 0.0 | -0.873792 | -0.660531 | -1.716872 | -1.026167 | 0.845911 | 0.246200 | 0.948094 | -0.315810 | 2.125136 | 2.402628 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 1.892537 | -0.620189 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -1.155935 |
| 376 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 0.930938 | 0.593216 | -0.891688 | 0.0 | -0.872131 | 0.254625 | 1.038693 | 0.379672 | -0.057788 | 1.153254 | -0.332939 | 0.077142 | 0.523316 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.864013 | -0.620189 | -1.077862 | -0.001333 | 0.764998 | -0.679146 | 0.806541 |
| 379 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | 1.260659 | -0.887515 | 0.085049 | 0.0 | -0.863823 | 0.254625 | 1.530758 | 0.379672 | 1.749610 | 1.153254 | 2.157948 | 1.257124 | -0.277594 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.406799 | -0.620189 | 0.338096 | -0.327893 | -0.063296 | -0.368715 | -0.595227 |
| 392 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.045899 | -0.517332 | -0.891688 | 0.0 | -0.835575 | -1.575686 | 0.989486 | 0.379672 | 2.653309 | -1.567907 | 2.741627 | -0.815714 | 0.523316 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.635406 | 0.931603 | -1.077862 | -0.491174 | -0.615492 | -0.368715 | -0.595227 |
| 401 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | 0.256622 | -0.393938 | 0.085049 | 0.0 | -0.818959 | 0.254625 | 0.989486 | 1.785511 | 1.749610 | -1.567907 | 1.425534 | 0.554138 | 2.525591 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 2.589994 | 3.178192 | -2.171982 | -1.077862 | -0.001333 | 0.764998 | 1.493867 | 0.806541 |
| 406 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.650871 | -1.198611 | -0.764121 | 0.085049 | 0.0 | -0.800681 | 1.169781 | -1.323220 | -1.026167 | 0.845911 | 0.246200 | 0.311508 | 0.744290 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.149668 | 0.931603 | 0.338096 | -0.327893 | -0.063296 | -0.679146 | -0.034520 |
| 408 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | 1.704418 | -0.640727 | -0.891688 | 0.0 | -0.795696 | 1.169781 | -1.766079 | 0.379672 | 1.749610 | 1.153254 | 2.135850 | -0.562600 | -0.277594 | -0.603911 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.535365 | -0.620189 | -2.493820 | -0.327893 | -0.615492 | -0.368715 | -0.034520 |
| 424 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.198380 | -1.468833 | 2.444129 | 0.085049 | 0.0 | -0.759140 | -1.575686 | -0.486709 | -1.026167 | 1.749610 | 1.153254 | 1.618040 | 1.094659 | 0.122861 | -0.877232 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | -1.077862 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 429 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 0.511970 | -0.887515 | 0.085049 | 0.0 | -0.747509 | -1.575686 | -0.732742 | 0.379672 | 1.749610 | 0.246200 | 2.413347 | -1.690019 | 1.324226 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.892537 | -0.620189 | -2.493820 | -0.654454 | -0.615492 | -0.679146 | -0.875581 |
| 455 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.429664 | 0.296288 | -1.010909 | 2.038524 | 0.0 | -0.686028 | -1.575686 | 1.432345 | 1.785511 | 1.749610 | 0.246200 | 2.057020 | 1.161556 | 0.523316 | 1.036019 | -0.426230 | 0.266233 | 0.0 | 0.241988 | -0.164511 | -0.620189 | 0.338096 | -0.164613 | -0.891589 | -0.679146 | 0.245834 |
| 465 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 0.573948 | 1.086793 | -1.868426 | 0.0 | -0.666089 | -1.575686 | 0.989486 | 0.379672 | 0.845911 | 0.246200 | 0.855029 | -0.748957 | 0.923771 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.149668 | 0.155707 | -1.077862 | -0.817734 | -0.615492 | -0.368715 | -0.595227 |
| 489 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.665354 | -0.050786 | -0.393938 | 1.061787 | 0.0 | -0.604609 | -0.660531 | -0.781948 | -1.026167 | 1.749610 | 1.153254 | 2.151148 | -1.636192 | 0.523316 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.249709 | 0.155707 | -1.077862 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 492 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | 1.074726 | -1.010909 | 1.061787 | 0.0 | -0.599624 | 1.169781 | -1.274014 | -1.026167 | 1.749610 | -1.567907 | 1.890862 | 0.517738 | 1.724681 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.249709 | 0.155707 | -2.493820 | -0.654454 | -0.615492 | -0.679146 | -0.595227 |
| 497 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.774856 | 1.270575 | -0.764121 | 1.061787 | 0.0 | -0.587992 | 1.169781 | -1.520046 | 0.379672 | 2.653309 | 1.153254 | 2.764362 | -0.696395 | 0.523316 | -0.877232 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.892537 | -0.620189 | 1.754054 | -0.817734 | -0.615492 | -0.679146 | -0.875581 |
| 510 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | -1.005241 | 1.210187 | 1.061787 | 0.0 | -0.543128 | 0.254625 | 1.087899 | 0.379672 | 0.845911 | -0.660853 | 0.897099 | -1.065877 | -0.277594 | -0.057267 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.606882 | -1.396086 | 1.754054 | 0.325228 | 0.764998 | 1.493867 | -0.875581 |
| 534 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 0.415285 | -0.270544 | 0.085049 | 0.0 | -0.489956 | 0.254625 | -0.339090 | 1.785511 | 1.749610 | 0.246200 | 1.765925 | -0.112307 | 0.122861 | 1.036019 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.663930 | 0.155707 | 0.338096 | -0.327893 | -0.891589 | -0.368715 | -0.314873 |
| 536 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.526886 | 0.933417 | 0.840004 | 1.061787 | 0.0 | -0.486633 | -1.575686 | 0.891073 | 0.379672 | -0.057788 | -1.567907 | -0.233287 | -0.335767 | 2.125136 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | -0.164511 | -1.396086 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 541 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.101159 | -0.930867 | -0.147150 | 0.085049 | 0.0 | -0.470017 | -1.575686 | -0.142264 | 1.785511 | 0.845911 | -1.567907 | 1.107028 | 0.846323 | 2.525591 | -0.330589 | -0.426230 | -1.584178 | 0.0 | 0.241988 | -0.164511 | -0.620189 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | 0.245834 |
| 552 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | 0.073169 | -0.023755 | 0.085049 | 0.0 | -0.436784 | 0.254625 | 0.743454 | 0.379672 | 1.749610 | 1.153254 | 0.977416 | 0.858269 | 1.724681 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 2.406799 | -1.396086 | -1.077862 | 0.488508 | 0.764998 | -0.368715 | -0.875581 |
| 568 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -0.192095 | -0.887515 | 0.085049 | 0.0 | -0.395244 | 1.169781 | 0.595834 | 0.379672 | 2.653309 | -1.567907 | 2.837879 | 0.967750 | 0.923771 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.635406 | -0.620189 | 0.338096 | -0.327893 | -0.615492 | -0.368715 | -0.034520 |
| 575 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.869874 | -1.057302 | 1.210187 | 1.061787 | 0.0 | -0.375304 | 1.169781 | 1.432345 | 0.379672 | -0.057788 | -1.567907 | -0.216289 | 1.174486 | 2.525591 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 1.415991 | -0.293077 | 0.931603 | 0.338096 | -0.327893 | -0.339394 | -0.368715 | -0.034520 |
| 588 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | -0.276384 | -0.887515 | 0.085049 | 0.0 | -0.348718 | 0.254625 | -0.093057 | 0.379672 | 1.749610 | 0.246200 | 2.366177 | -1.044515 | 0.923771 | 0.216054 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.406799 | 0.155707 | 0.338096 | -0.491174 | -0.339394 | -0.679146 | -0.314873 |
| 609 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | -0.529253 | 0.593216 | -0.891688 | 0.0 | -0.302192 | -0.660531 | 1.383138 | 0.379672 | 1.749610 | -1.567907 | 2.264187 | -1.280764 | 1.324226 | 2.402628 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | -0.491174 | -0.891589 | -0.368715 | -1.155935 |
| 624 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.760373 | -0.350758 | -0.270544 | -0.891688 | 0.0 | -0.270622 | -1.575686 | 0.595834 | -1.026167 | 0.845911 | 1.153254 | 0.941508 | 0.899729 | 1.724681 | 0.762698 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 3.049627 | 0.155707 | 0.338096 | -0.327893 | -0.615492 | -0.679146 | -0.034520 |
| 625 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.884358 | 0.316121 | -0.023755 | 0.085049 | 0.0 | -0.267298 | 1.169781 | 0.399008 | 0.379672 | 0.845911 | -1.567907 | 0.904749 | 0.692290 | 0.523316 | -0.877232 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.864013 | -0.620189 | 0.338096 | -0.327893 | -0.063296 | -0.679146 | -0.595227 |
| 627 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | 0.216957 | 1.950552 | 1.061787 | 0.0 | -0.262313 | 0.254625 | 0.743454 | -1.026167 | 1.749610 | 1.153254 | 1.555996 | 0.662636 | 0.122861 | 1.855984 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 2.535365 | 0.155707 | 0.338096 | 0.325228 | 1.041095 | -0.679146 | -1.155935 |
| 646 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.760373 | 0.162417 | -0.147150 | 0.085049 | 0.0 | -0.212465 | -1.575686 | 0.349801 | 0.379672 | 1.749610 | 1.153254 | 1.133163 | 1.191210 | 0.923771 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.149668 | 0.155707 | 0.338096 | -0.817734 | -1.167687 | -0.058285 | -0.595227 |
| 674 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | 1.163974 | -0.517332 | 0.085049 | 0.0 | -0.141015 | 0.254625 | -1.421633 | -1.026167 | 0.845911 | -0.660853 | 0.860341 | -0.710871 | -0.277594 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.635406 | 0.155707 | 0.338096 | -0.164613 | -1.167687 | -0.679146 | -0.034520 |
| 706 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 0.336849 | 1.677148 | 1.827158 | 0.085049 | 0.0 | -0.064580 | -0.660531 | 1.678377 | 1.785511 | 1.749610 | -0.660853 | 1.421709 | 0.387597 | 0.523316 | 0.216054 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.378275 | -0.620189 | -1.077862 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 714 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 0.802025 | -1.010909 | -0.891688 | 0.0 | -0.046302 | 1.169781 | 0.005356 | 0.379672 | 1.749610 | 1.153254 | 2.315182 | -1.081899 | 2.525591 | 1.855984 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 2.663930 | -1.396086 | -1.077862 | -0.327893 | -0.063296 | -0.368715 | -0.314873 |
| 721 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 0.338433 | 1.827158 | 0.085049 | 0.0 | -0.033009 | 1.169781 | 1.432345 | 0.379672 | 1.749610 | 0.246200 | 1.587230 | -1.426786 | 0.122861 | 0.762698 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.378275 | -0.620189 | 0.338096 | 0.815068 | 1.869389 | -0.368715 | 0.245834 |
| 723 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.586272 | -0.270544 | -0.891688 | 0.0 | -0.029685 | 1.169781 | 0.448214 | 0.379672 | 0.845911 | 0.246200 | 0.922597 | 1.390638 | 1.324226 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.221185 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | 0.806541 |
| 728 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 1.582942 | 0.963398 | 0.085049 | 0.0 | -0.019716 | 0.254625 | -0.486709 | 0.379672 | 0.845911 | 0.246200 | 0.900074 | -0.052015 | 2.125136 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.378275 | -0.620189 | 0.338096 | 0.488508 | 0.764998 | -0.679146 | 1.086895 |
| 741 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | -1.332482 | -0.517332 | -0.891688 | 0.0 | 0.006870 | 1.169781 | 1.186312 | 0.379672 | 2.653309 | 0.246200 | 2.507263 | -0.919574 | 1.324226 | -0.603911 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.249709 | 0.155707 | 1.754054 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 743 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 2.417384 | -0.216886 | -0.887515 | 0.085049 | 0.0 | 0.011855 | 0.254625 | 0.152975 | -1.026167 | 1.749610 | 1.153254 | 1.534748 | 1.056291 | 0.122861 | -0.603911 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 2.406799 | 0.931603 | 0.338096 | -0.327893 | -0.339394 | 0.562576 | -0.314873 |
| 745 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -1.654765 | 1.086793 | 1.061787 | 0.0 | 0.015178 | 0.254625 | -0.191470 | 0.379672 | -0.057788 | -0.660853 | -0.025058 | -0.185669 | 0.122861 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 0.735447 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 1.183437 | 0.806541 |
| 755 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | 1.069768 | 0.223033 | -0.891688 | 0.0 | 0.033456 | 1.169781 | 1.186312 | 0.379672 | 1.749610 | 1.153254 | 2.368514 | -1.252094 | 0.122861 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.892537 | 0.931603 | 1.754054 | 0.325228 | -0.339394 | -0.368715 | -0.875581 |
| 758 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.417384 | 0.710298 | -1.010909 | -0.891688 | 0.0 | 0.038441 | -0.660531 | 0.005356 | 0.379672 | 0.845911 | 1.153254 | 1.147612 | -0.460286 | 0.122861 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.349751 | -1.396086 | -2.493820 | -0.164613 | -0.063296 | -0.679146 | -0.034520 |
| 766 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 1.639962 | -0.887515 | 1.061787 | 0.0 | 0.060042 | -0.660531 | -0.191470 | 0.379672 | 2.653309 | 0.246200 | 2.705718 | -0.205204 | -0.277594 | -1.150554 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.278234 | -0.620189 | -1.077862 | 0.161947 | -0.891589 | 1.493867 | 0.806541 |
| 770 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.923430 | -1.010909 | 1.061787 | 0.0 | 0.073335 | 1.169781 | -1.274014 | 0.379672 | 2.653309 | 1.153254 | 2.788584 | 1.002323 | 2.525591 | 0.489376 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 1.506840 | -2.171982 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 771 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.692944 | -0.887515 | 1.061787 | 0.0 | 0.074997 | 0.254625 | -1.224807 | -1.026167 | 0.845911 | 0.246200 | 0.888813 | -0.832157 | 1.324226 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.221185 | 0.931603 | 0.338096 | 0.325228 | -0.063296 | 1.493867 | -1.155935 |
| 774 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -0.888723 | -0.887515 | -1.868426 | 0.0 | 0.081644 | 0.254625 | -1.274014 | -1.026167 | 1.749610 | -1.567907 | 2.178558 | 0.423014 | 1.724681 | -0.057267 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 2.535365 | 0.155707 | 1.754054 | 0.325228 | 0.764998 | 1.183437 | -0.595227 |
| 779 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.541369 | 1.290408 | -0.640727 | 1.061787 | 0.0 | 0.093275 | -1.575686 | -1.569253 | 0.379672 | -0.961486 | 0.246200 | -0.858824 | -0.559508 | 2.525591 | -0.877232 | -0.426230 | 0.266233 | 0.0 | 2.589994 | 0.864013 | -0.620189 | 1.754054 | 0.488508 | -1.167687 | -0.058285 | 0.806541 |
| 787 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | 0.715256 | -0.887515 | -1.868426 | 0.0 | 0.118199 | 1.169781 | -0.043851 | 0.379672 | 0.845911 | -0.660853 | 0.950432 | 0.210797 | 0.122861 | 0.762698 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.506840 | 0.931603 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.595227 |
| 789 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.774856 | 1.421801 | -1.010909 | -0.891688 | 0.0 | 0.121523 | -0.660531 | 1.235519 | -1.026167 | 0.845911 | -1.567907 | 0.845467 | -1.684679 | 2.525591 | -0.330589 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.635406 | -1.396086 | 0.338096 | 2.121310 | 0.488900 | 0.252146 | 0.526188 |
| 804 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | 1.139183 | -1.010909 | 1.061787 | 0.0 | 0.151432 | -1.575686 | -1.520046 | 1.785511 | 1.749610 | 1.153254 | 2.205968 | 0.258721 | -0.277594 | 1.855984 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 2.021103 | 0.155707 | -1.077862 | -0.327893 | -0.063296 | -0.058285 | -0.875581 |
| 806 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.650871 | 0.474783 | -0.270544 | 1.061787 | 0.0 | 0.154755 | -0.660531 | 1.038693 | 0.379672 | 0.845911 | -0.660853 | 0.837605 | 0.141791 | 1.724681 | 1.036019 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.864013 | 0.931603 | 0.338096 | 0.161947 | 0.488900 | 0.562576 | -1.155935 |
| 812 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 0.692944 | 2.197341 | 0.085049 | 0.0 | 0.168048 | 0.254625 | 0.841867 | 0.379672 | 0.845911 | -1.567907 | 0.917285 | -0.390437 | 2.125136 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.864013 | -1.396086 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | -0.875581 |
| 821 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.558080 | -0.147150 | 1.061787 | 0.0 | 0.187988 | 1.169781 | -0.486709 | -1.026167 | 1.749610 | -0.660853 | 1.405986 | -0.342091 | 1.324226 | 0.489376 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.378275 | 0.155707 | 0.338096 | 0.325228 | 1.041095 | -0.058285 | -0.314873 |
| 851 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | -0.209449 | -0.640727 | 1.061787 | 0.0 | 0.276054 | 1.169781 | 1.284725 | 0.379672 | 2.653309 | -1.567907 | 2.855728 | 0.598971 | 0.523316 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 2.149668 | -0.620189 | 0.338096 | -0.327893 | -0.615492 | 0.562576 | -0.595227 |
| 867 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.431867 | 1.533360 | -0.887515 | 0.085049 | 0.0 | 0.315933 | 1.169781 | -1.766079 | 0.379672 | 1.749610 | -1.567907 | 2.412285 | -0.677843 | -0.277594 | 1.855984 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 2.663930 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 869 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | 1.605254 | 0.716610 | -0.891688 | 0.0 | 0.319256 | 1.169781 | -0.683535 | 0.379672 | 2.653309 | -0.660853 | 2.672571 | -0.486848 | 0.923771 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 1.763972 | -0.620189 | 0.338096 | -0.491174 | -0.615492 | -0.679146 | -0.314873 |
| 887 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.555852 | -0.854015 | 2.073946 | 2.038524 | 0.0 | 0.360797 | -1.575686 | -0.289883 | 0.379672 | 0.845911 | -1.567907 | 1.421072 | 1.260357 | 0.122861 | 0.489376 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.121144 | 2.483396 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 890 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | 0.405369 | -1.010909 | 1.061787 | 0.0 | 0.365782 | 1.169781 | -0.634329 | 0.379672 | 0.845911 | 0.246200 | 0.849717 | -0.654092 | 1.724681 | 0.489376 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.792496 | -0.620189 | -2.493820 | -0.327893 | -0.063296 | -0.368715 | -0.034520 |
| 898 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.665354 | 0.291330 | -0.764121 | 0.085049 | 0.0 | 0.382398 | 0.254625 | 1.481551 | -2.432006 | 2.653309 | 1.153254 | 2.812595 | 0.605998 | 0.122861 | -0.330589 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.763972 | -0.620189 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | 0.806541 |
| 899 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.732610 | -0.640727 | -0.891688 | 0.0 | 0.384060 | -1.575686 | 0.940280 | -1.026167 | 2.653309 | 0.246200 | 2.593954 | -0.307659 | -0.277594 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.506840 | -0.620189 | 1.754054 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 904 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | -0.216886 | -1.010909 | 0.085049 | 0.0 | 0.395691 | 1.169781 | 0.497421 | -1.026167 | 2.653309 | 1.153254 | 2.499189 | -0.784233 | 1.324226 | -0.877232 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.763972 | 0.155707 | 1.754054 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 916 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -1.572955 | -0.640727 | -0.891688 | 0.0 | 0.423939 | 1.169781 | -1.618459 | -1.026167 | 2.653309 | -0.660853 | 2.610527 | -0.613757 | -0.277594 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.892537 | -0.620189 | 0.338096 | 0.651788 | -0.063296 | -0.679146 | 1.086895 |
| 919 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.417384 | 1.553193 | 1.086793 | 1.061787 | 0.0 | 0.428924 | 1.169781 | 0.054562 | 0.379672 | 0.845911 | 1.153254 | 0.851842 | 0.799523 | 1.324226 | -0.877232 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 1.763972 | 2.483396 | -1.077862 | 0.325228 | 0.764998 | 0.873006 | -0.034520 |
| 936 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.884358 | -1.087051 | 1.950552 | 0.085049 | 0.0 | 0.467141 | -0.660531 | 0.841867 | 0.379672 | 2.653309 | -0.660853 | 2.455843 | -0.179626 | 0.122861 | 1.855984 | 2.346151 | 0.266233 | 0.0 | -0.932014 | 1.378275 | 0.931603 | 0.338096 | -1.144294 | -1.167687 | -0.679146 | -1.155935 |
| 945 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | 1.287929 | 2.320735 | 0.085049 | 0.0 | 0.485419 | 1.169781 | -1.126394 | 0.379672 | 1.749610 | -1.567907 | 2.204906 | 1.139632 | 0.523316 | -1.150554 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.763972 | -0.620189 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.595227 |
| 947 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.650871 | 0.564031 | -0.517332 | 0.085049 | 0.0 | 0.488742 | -0.660531 | -0.093057 | 0.379672 | 0.845911 | -0.660853 | 0.412861 | 1.014832 | 2.525591 | 1.036019 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.164511 | -0.620189 | -1.077862 | 0.161947 | 0.764998 | 1.493867 | 0.806541 |
| 956 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 2.088878 | -1.478749 | -0.147150 | 1.061787 | 0.0 | 0.520313 | 1.169781 | 1.629171 | 0.379672 | 2.653309 | -0.660853 | 2.807708 | -1.446321 | 1.324226 | -0.330589 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 3.178192 | 0.931603 | 0.338096 | -0.001333 | -0.339394 | 1.493867 | 0.806541 |
| 966 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | -0.499504 | -0.270544 | 1.061787 | 0.0 | 0.556869 | 0.254625 | -0.634329 | -1.026167 | 0.845911 | -1.567907 | 0.744752 | -0.321853 | 1.724681 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.535365 | -2.171982 | -1.077862 | 0.488508 | 1.317193 | 0.873006 | 1.367249 |
| 971 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 1.493695 | 0.223033 | -0.891688 | 0.0 | 0.568500 | 1.169781 | 0.792660 | -1.026167 | 1.749610 | -0.660853 | 1.410660 | 1.423103 | 0.122861 | 0.216054 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 2.278234 | -1.396086 | -1.077862 | -0.327893 | -0.615492 | -0.679146 | -0.314873 |
| 987 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | -0.648250 | -0.887515 | 0.085049 | 0.0 | 0.608380 | -0.660531 | -0.683535 | 0.379672 | 0.845911 | -0.660853 | 0.869690 | 0.152051 | -0.277594 | -1.150554 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 0.349751 | 1.707500 | 0.338096 | -0.491174 | -0.615492 | 0.252146 | -0.595227 |
| 994 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.650871 | -1.191173 | 2.320735 | -0.891688 | 0.0 | 0.624996 | 1.169781 | -0.339090 | 1.785511 | 1.749610 | 0.246200 | 1.432971 | -0.643973 | -0.277594 | -1.150554 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.635406 | 0.155707 | -1.077862 | -0.327893 | -0.339394 | -0.679146 | -0.595227 |
| 1010 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | 0.826816 | -1.010909 | 1.061787 | 0.0 | 0.663213 | -0.660531 | 0.743454 | 1.785511 | 1.749610 | 1.153254 | 1.748501 | -0.266902 | -0.277594 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 1.415991 | 2.535365 | 0.931603 | 1.754054 | -0.001333 | 0.764998 | -0.679146 | -1.155935 |
| 1031 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -1.054823 | -0.023755 | 0.085049 | 0.0 | 0.718047 | -1.575686 | -0.683535 | 0.379672 | 0.845911 | 1.153254 | 0.763450 | 0.235110 | 0.523316 | -1.150554 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 2.149668 | -1.396086 | 1.754054 | -0.001333 | 0.764998 | 0.562576 | -0.314873 |
| 1034 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.884358 | 0.583864 | 1.333581 | 0.085049 | 0.0 | 0.723032 | -0.660531 | 1.432345 | -2.432006 | 0.845911 | -1.567907 | 0.923872 | 0.779988 | -0.277594 | 0.762698 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.635406 | -0.620189 | 0.338096 | -0.001333 | 0.764998 | -0.679146 | 0.806541 |
| 1054 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 1.704418 | -0.270544 | 1.061787 | 0.0 | 0.762911 | 0.254625 | -1.520046 | 0.379672 | 0.845911 | -0.660853 | 0.842067 | 0.932475 | 0.122861 | -0.330589 | -0.426230 | -0.658973 | 0.0 | 1.415991 | 2.278234 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | 0.806541 |
| 1080 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -1.424209 | -0.764121 | 0.085049 | 0.0 | 0.834361 | 0.254625 | -0.732742 | 0.379672 | 1.749610 | -0.660853 | 2.146686 | -0.412221 | 2.125136 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 1.506840 | -0.620189 | 1.754054 | 0.978348 | 2.145487 | 0.873006 | -0.875581 |
| 1103 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | -0.769726 | 0.840004 | 1.061787 | 0.0 | 0.884210 | 0.254625 | 1.481551 | 0.379672 | -0.057788 | 0.246200 | -0.013584 | -0.087150 | 2.125136 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.864013 | -0.620189 | 0.338096 | 0.161947 | 0.764998 | 1.493867 | 0.806541 |
| 1126 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | -1.334961 | -0.023755 | 0.085049 | 0.0 | 0.940705 | 0.254625 | -0.339090 | 0.379672 | 2.653309 | 0.246200 | 2.725691 | 0.731642 | 0.523316 | 0.216054 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.021103 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 1129 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | 0.635925 | -0.023755 | -0.891688 | 0.0 | 0.947352 | 1.169781 | 1.333932 | -1.026167 | 2.653309 | 1.153254 | 2.785610 | 1.693363 | 2.125136 | 1.855984 | 2.346151 | 1.191438 | 0.0 | -0.932014 | 1.635406 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -0.875581 |
| 1154 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 0.925980 | 2.073946 | 1.061787 | 0.0 | 0.997200 | 1.169781 | 1.579964 | 0.379672 | 2.653309 | 0.246200 | 2.795171 | -1.277953 | 0.122861 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.021103 | -0.620189 | 0.338096 | -0.327893 | -0.615492 | -0.368715 | -1.155935 |
| 1166 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.212863 | -1.084572 | -0.640727 | 2.038524 | 0.0 | 1.028771 | 0.254625 | 1.137106 | -1.026167 | 1.749610 | 1.153254 | 1.848366 | -1.224267 | -0.277594 | 2.675949 | 2.346151 | -0.658973 | 0.0 | 0.241988 | 1.506840 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 1176 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | -1.243234 | 1.580370 | 1.061787 | 0.0 | 1.047049 | -1.575686 | 0.300595 | 0.379672 | 1.749610 | -0.660853 | 2.105678 | -1.519965 | 0.122861 | 0.216054 | -0.426230 | -0.658973 | 0.0 | 1.415991 | 2.021103 | -0.620189 | 0.338096 | -0.491174 | -0.615492 | -0.368715 | -0.595227 |
| 1194 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 1.047456 | -0.887515 | 1.061787 | 0.0 | 1.081943 | -0.660531 | -0.929568 | 1.785511 | 1.749610 | -0.660853 | 2.011975 | 0.951869 | 1.324226 | -0.330589 | -0.426230 | 0.266233 | 0.0 | 2.589994 | 2.278234 | -0.620189 | 0.338096 | -0.654454 | -0.615492 | -0.368715 | -0.595227 |
| 1195 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.016150 | -1.010909 | 0.085049 | 0.0 | 1.083605 | 0.254625 | -1.470840 | 0.379672 | 1.749610 | 0.246200 | 1.885975 | 1.134291 | 0.523316 | -0.330589 | -0.426230 | -1.584178 | 0.0 | -0.932014 | 1.506840 | -0.620189 | 0.338096 | 0.161947 | 0.764998 | -0.679146 | -1.155935 |
| 1209 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 2.526886 | -1.072176 | -1.010909 | 1.061787 | 0.0 | 1.116838 | 0.254625 | 1.284725 | -2.432006 | 0.845911 | 1.153254 | 0.930671 | 0.864874 | 0.122861 | 1.309341 | 2.346151 | 0.266233 | 0.0 | 0.241988 | 0.992578 | -0.620189 | 1.754054 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 1231 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0.993860 | -0.211928 | 0.469821 | 1.061787 | 0.0 | 1.166686 | 0.254625 | -1.569253 | 0.379672 | -0.057788 | -0.660853 | -0.199928 | -0.648751 | 1.324226 | -0.330589 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.992578 | 0.155707 | 0.338096 | 0.488508 | 0.764998 | -0.679146 | 1.367249 |
| 1235 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.993860 | 1.176369 | -0.887515 | 0.085049 | 0.0 | 1.174995 | 0.254625 | 0.399008 | 0.379672 | 0.845911 | 1.153254 | 0.821245 | -1.225110 | 0.523316 | -0.877232 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.221185 | 1.707500 | -1.077862 | 0.488508 | 0.488900 | -0.679146 | -0.314873 |
| 1264 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.979376 | -0.804433 | -0.887515 | 0.085049 | 0.0 | 1.238136 | 0.254625 | -0.289883 | -1.026167 | 2.653309 | -1.567907 | 2.663434 | 0.771836 | 2.125136 | -0.877232 | -0.426230 | -0.658973 | 0.0 | 2.589994 | 2.921061 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 1268 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.760373 | -0.350758 | -1.010909 | 1.061787 | 0.0 | 1.246445 | -1.575686 | -0.289883 | -1.026167 | 1.749610 | 0.246200 | 1.373052 | 1.123610 | 0.523316 | 1.309341 | 2.346151 | 1.191438 | 0.0 | 2.589994 | 2.021103 | -0.620189 | -1.077862 | -0.654454 | -0.615492 | -0.679146 | -0.595227 |
| 1275 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.541369 | 0.345870 | -0.764121 | 0.085049 | 0.0 | 1.264722 | -1.575686 | -0.634329 | 0.379672 | 0.845911 | 0.246200 | 1.405136 | 1.218616 | -0.277594 | -1.150554 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 0.478316 | -0.620189 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 1277 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0.993860 | -0.169783 | -0.887515 | 1.061787 | 0.0 | 1.269707 | 0.254625 | -0.978775 | 0.379672 | 2.653309 | 1.153254 | 2.725053 | -0.013366 | 1.724681 | 0.489376 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 1.635406 | 0.155707 | 0.338096 | -0.817734 | -0.891589 | -0.058285 | -0.595227 |
| 1278 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.101159 | 1.439154 | 0.099639 | 0.085049 | 0.0 | 1.271369 | 1.169781 | 1.186312 | 0.379672 | 0.845911 | -1.567907 | 0.386301 | 1.635320 | 1.724681 | -0.603911 | -0.426230 | 1.191438 | 0.0 | 0.241988 | 0.478316 | -1.396086 | 0.338096 | 0.815068 | 1.041095 | 0.873006 | 0.806541 |
| 1305 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.869874 | -0.920951 | -0.270544 | 1.061787 | 0.0 | 1.337834 | 1.169781 | 0.103769 | 0.379672 | -0.057788 | 1.153254 | 0.074595 | 0.194354 | 0.523316 | -0.057267 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.349751 | -0.620189 | -1.077862 | -0.001333 | -0.891589 | -0.368715 | 0.806541 |
| 1310 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | 1.025144 | 0.716610 | 1.061787 | 0.0 | 1.349466 | -1.575686 | 1.038693 | 0.379672 | 1.749610 | 0.246200 | 1.972666 | 1.027480 | -0.277594 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 1.506840 | 0.155707 | 0.338096 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 1357 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0.555852 | 1.471383 | -0.393938 | 0.085049 | 0.0 | 1.472426 | 0.254625 | 0.841867 | 0.379672 | 0.845911 | -1.567907 | 1.454431 | 0.074332 | 2.525591 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 0.864013 | 0.155707 | 1.754054 | 0.978348 | 0.764998 | 0.873006 | 0.806541 |
| 1370 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.103362 | 0.908626 | 0.593216 | 1.061787 | 0.0 | 1.510643 | 0.254625 | -0.093057 | 0.379672 | -0.057788 | -0.660853 | -0.220113 | -1.712927 | 2.125136 | 0.762698 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 0.606882 | 0.931603 | 1.754054 | 0.161947 | 0.764998 | -0.368715 | 0.806541 |
| 1374 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 2.307882 | -0.489587 | 1.456975 | 0.085049 | 0.0 | 1.517290 | 1.169781 | 0.300595 | 0.379672 | 1.749610 | 1.153254 | 2.416322 | -0.358675 | 0.523316 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | 2.278234 | -0.620189 | -1.077862 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 1377 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1.322365 | 0.648321 | -0.887515 | -1.868426 | 0.0 | 1.522275 | -0.660531 | -1.175601 | 0.379672 | 2.653309 | 1.153254 | 2.689569 | -0.080826 | 0.122861 | -0.057267 | -0.426230 | 1.191438 | 0.0 | -0.932014 | 2.149668 | 0.155707 | 0.338096 | -0.327893 | -0.063296 | 0.562576 | -0.314873 |
| 1396 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1.760373 | 0.906147 | 1.827158 | 1.061787 | 0.0 | 1.567139 | -1.575686 | 0.005356 | 0.379672 | 0.845911 | -1.567907 | 0.838243 | -1.190396 | 1.324226 | -0.603911 | -0.426230 | -0.658973 | 0.0 | -0.932014 | 0.478316 | -0.620189 | -1.077862 | -0.817734 | -0.615492 | -0.058285 | -0.595227 |
| 1401 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.979376 | -1.520894 | 2.073946 | 1.061787 | 0.0 | 1.575447 | 0.254625 | 0.251388 | 1.785511 | 2.653309 | -0.660853 | 2.790497 | 1.615925 | 0.523316 | 0.762698 | -0.426230 | -1.584178 | 0.0 | 0.241988 | 3.049627 | -2.171982 | 0.338096 | 0.488508 | 1.317193 | -0.368715 | -0.034520 |
| 1437 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0.227347 | -1.729138 | -0.023755 | 0.085049 | 0.0 | 1.656867 | 1.169781 | 1.038693 | 0.379672 | 2.653309 | 1.153254 | 2.746939 | 0.138980 | -0.277594 | -0.603911 | -0.426230 | 0.266233 | 0.0 | -0.932014 | 1.249709 | 0.155707 | -1.077862 | -0.164613 | -1.167687 | -0.368715 | -0.314873 |
| 1461 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1.431867 | -0.973012 | 2.320735 | 0.085049 | 0.0 | 1.711700 | 1.169781 | -1.323220 | -1.026167 | 0.845911 | -1.567907 | 0.924509 | 0.319435 | 0.523316 | -0.603911 | -0.426230 | -0.658973 | 0.0 | 0.241988 | 1.121144 | 0.155707 | 0.338096 | -0.654454 | -0.615492 | -0.058285 | -1.155935 |
index_4 = np.where(y_means==4)
index_4
(array([ 3, 7, 10, 12, 13, 14, 16, 17, 20, 21, 23,
24, 30, 32, 34, 37, 39, 40, 41, 42, 47, 49,
51, 57, 68, 69, 71, 72, 74, 81, 84, 86, 96,
97, 100, 101, 102, 107, 108, 109, 113, 118, 125, 127,
128, 138, 140, 143, 145, 146, 148, 149, 160, 161, 162,
164, 169, 170, 171, 176, 177, 179, 180, 181, 182, 191,
200, 202, 206, 207, 214, 217, 221, 224, 229, 238, 239,
241, 246, 252, 255, 259, 260, 262, 264, 267, 272, 274,
278, 284, 288, 289, 292, 294, 296, 301, 302, 309, 312,
318, 320, 323, 328, 331, 335, 336, 337, 345, 349, 350,
351, 357, 362, 363, 369, 370, 373, 377, 380, 381, 382,
383, 385, 397, 399, 402, 404, 405, 407, 410, 414, 415,
416, 418, 419, 421, 422, 430, 436, 437, 443, 449, 453,
457, 460, 461, 463, 470, 474, 475, 476, 478, 479, 480,
481, 485, 487, 490, 494, 495, 496, 498, 501, 505, 507,
512, 513, 515, 516, 517, 521, 522, 546, 550, 553, 555,
563, 565, 571, 574, 576, 577, 579, 580, 581, 585, 586,
589, 598, 602, 606, 613, 614, 615, 618, 620, 629, 630,
631, 633, 634, 637, 638, 640, 642, 644, 645, 655, 656,
657, 659, 662, 663, 666, 668, 670, 671, 673, 680, 682,
683, 688, 689, 694, 697, 698, 709, 711, 712, 720, 722,
724, 725, 726, 727, 731, 732, 733, 734, 739, 740, 754,
762, 763, 764, 765, 769, 776, 777, 781, 786, 793, 794,
797, 798, 800, 802, 803, 808, 815, 818, 819, 820, 823,
826, 827, 828, 831, 833, 834, 835, 841, 842, 848, 850,
853, 856, 859, 860, 863, 868, 871, 876, 880, 885, 892,
893, 902, 906, 909, 910, 911, 912, 915, 917, 921, 924,
929, 933, 934, 939, 940, 952, 965, 967, 972, 974, 977,
980, 981, 982, 984, 991, 993, 996, 998, 1001, 1003, 1004,
1012, 1016, 1017, 1021, 1022, 1025, 1027, 1032, 1039, 1041, 1045,
1047, 1052, 1056, 1059, 1060, 1061, 1064, 1068, 1069, 1070, 1072,
1082, 1091, 1097, 1100, 1102, 1104, 1108, 1110, 1115, 1118, 1125,
1127, 1132, 1134, 1136, 1137, 1139, 1141, 1149, 1151, 1152, 1153,
1168, 1169, 1170, 1175, 1178, 1180, 1182, 1189, 1193, 1197, 1198,
1201, 1205, 1213, 1215, 1217, 1222, 1224, 1227, 1230, 1233, 1238,
1239, 1245, 1247, 1248, 1249, 1252, 1256, 1258, 1266, 1271, 1272,
1273, 1276, 1283, 1297, 1302, 1307, 1309, 1311, 1312, 1313, 1317,
1320, 1323, 1329, 1332, 1337, 1338, 1339, 1343, 1345, 1349, 1353,
1365, 1367, 1375, 1376, 1379, 1381, 1382, 1383, 1387, 1390, 1400,
1402, 1407, 1408, 1411, 1413, 1415, 1417, 1420, 1423, 1426, 1428,
1433, 1436, 1438, 1449, 1458, 1460, 1464, 1467, 1469], dtype=int64),)
Group_4 = x.iloc[index_4]
Group_4
| BusinessTravel_Non-Travel | BusinessTravel_Travel_Frequently | BusinessTravel_Travel_Rarely | Department_Human Resources | Department_Research & Development | Department_Sales | EducationField_Human Resources | EducationField_Life Sciences | EducationField_Marketing | EducationField_Medical | EducationField_Other | EducationField_Technical Degree | Gender_Female | Gender_Male | JobRole_Healthcare Representative | JobRole_Human Resources | JobRole_Laboratory Technician | JobRole_Manager | JobRole_Manufacturing Director | JobRole_Research Director | JobRole_Research Scientist | JobRole_Sales Executive | JobRole_Sales Representative | MaritalStatus_Divorced | MaritalStatus_Married | MaritalStatus_Single | Over18_Y | OverTime_No | OverTime_Yes | Age Group_Adult | Age Group_Child | Age Group_Old | Age | DailyRate | DistanceFromHome | Education | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | HourlyRate | JobInvolvement | JobLevel | JobSatisfaction | MonthlyIncome | MonthlyRate | NumCompaniesWorked | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -0.429664 | 1.461466 | -0.764121 | 1.061787 | 0.0 | -1.694636 | 1.169781 | -0.486709 | 0.379672 | -0.961486 | 0.246200 | -0.763634 | 1.243211 | -0.678049 | -1.150554 | -0.426230 | 0.266233 | 0.0 | -0.932014 | -0.421642 | 0.155707 | 0.338096 | 0.161947 | 0.764998 | 0.252146 | -1.155935 |
| 7 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.758170 | 1.377177 | 1.827158 | -1.868426 | 0.0 | -1.684667 | 1.169781 | 0.054562 | 0.379672 | -0.961486 | 0.246200 | -0.809529 | -0.137464 | -0.678049 | 1.855984 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -1.321601 | -0.620189 | 0.338096 | -0.981014 | -1.167687 | -0.679146 | -1.155935 |
| 10 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.210661 | 0.016150 | 0.840004 | 0.085049 | 0.0 | -1.679682 | -1.575686 | 0.891073 | 1.785511 | -0.961486 | -0.660853 | -0.866261 | 0.304397 | -1.078504 | -0.603911 | -0.426230 | 0.266233 | 0.0 | 0.241988 | -0.678774 | 1.707500 | 0.338096 | -0.327893 | -0.063296 | -0.679146 | -0.314873 |
| 12 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.648668 | -0.328446 | 2.073946 | -1.868426 | 0.0 | -1.676359 | -1.575686 | -1.716872 | 0.379672 | -0.961486 | 0.246200 | -0.763209 | 0.120429 | -0.678049 | 0.489376 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.807339 | -1.396086 | -1.077862 | -0.327893 | -0.615492 | 0.562576 | -0.314873 |
| 13 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | 1.347427 | 1.210187 | -0.891688 | 0.0 | -1.673035 | -0.660531 | 1.333932 | 0.379672 | -0.961486 | 1.153254 | -0.816328 | -0.780719 | -1.078504 | -1.150554 | -0.426230 | 0.266233 | 0.0 | 0.241988 | -1.064470 | -0.620189 | 0.338096 | -0.817734 | -0.615492 | -0.368715 | -0.595227 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1458 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.210661 | -1.277942 | -1.010909 | 1.061787 | 0.0 | 1.706715 | 0.254625 | -0.191470 | -2.432006 | -0.961486 | 1.153254 | -0.749185 | -0.753454 | -0.678049 | -0.877232 | -0.426230 | 1.191438 | 0.0 | 0.241988 | -0.935905 | 1.707500 | 0.338096 | -0.491174 | -0.339394 | -0.368715 | -0.875581 |
| 1460 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | -0.867672 | -0.829224 | 2.320735 | 1.061787 | 0.0 | 1.710039 | 1.169781 | 0.349801 | -1.026167 | -0.961486 | -1.567907 | -0.577502 | -0.818525 | -0.678049 | -0.330589 | -0.426230 | -0.658973 | 0.0 | -0.932014 | -0.807339 | 0.155707 | -2.493820 | -0.327893 | -0.063296 | -0.679146 | -0.034520 |
| 1464 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | -1.196177 | 0.903668 | -0.517332 | 0.085049 | 0.0 | 1.720008 | 1.169781 | -1.766079 | -1.026167 | -0.961486 | 0.246200 | -0.751522 | 0.992907 | -1.078504 | 0.762698 | -0.426230 | 1.191438 | 0.0 | -0.932014 | -0.807339 | -0.620189 | 0.338096 | -0.491174 | -0.615492 | -0.679146 | -1.155935 |
| 1467 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | -1.086676 | -1.605183 | -0.640727 | 0.085049 | 0.0 | 1.726655 | -0.660531 | 1.038693 | 1.785511 | -0.057788 | -0.660853 | -0.076690 | -1.284418 | -0.678049 | 1.309341 | 2.346151 | -0.658973 | 0.0 | 0.241988 | -0.678774 | -2.171982 | 0.338096 | -0.164613 | -0.615492 | -0.679146 | -0.314873 |
| 1469 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -0.320163 | -0.432568 | -0.147150 | 0.085049 | 0.0 | 1.733302 | -0.660531 | 0.792660 | 1.785511 | -0.057788 | 0.246200 | -0.445978 | -0.574124 | -0.277594 | -0.877232 | -0.426230 | -1.584178 | 0.0 | -0.932014 | -0.678774 | 0.155707 | 1.754054 | -0.491174 | -0.339394 | -0.368715 | -0.595227 |
449 rows × 58 columns
plt.scatter(X_principal.iloc[y_means==0,0],X_principal.iloc[y_means==0,1],s=25,c='yellow',label="Group_0")
plt.scatter(X_principal.iloc[y_means==1,0],X_principal.iloc[y_means==1,1],s=25,c='green',label="Group_1")
plt.scatter(X_principal.iloc[y_means==2,0],X_principal.iloc[y_means==2,1],s=25,c='blue',label="Group_2")
plt.scatter(X_principal.iloc[y_means==3,0],X_principal.iloc[y_means==3,1],s=25,c='purple',label="Group_3")
plt.scatter(X_principal.iloc[y_means==4,0],X_principal.iloc[y_means==4,1],s=25,c='black',label="Group_4")
plt.legend()
plt.show()
silhouette_score(X_principal,y_means)
0.4255702350140425
plt.figure(figsize =(8, 8))
plt.title('Visualising the data')
Dendrogram = shc.dendrogram((shc.linkage(X_principal, method ='ward')))
ac2 = AgglomerativeClustering(n_clusters = 3)
# Visualizing the clustering
plt.figure(figsize =(6, 6))
plt.scatter(X_principal['P1'], X_principal['P2'],
c = ac2.fit_predict(X_principal))
plt.show()
cluster = AgglomerativeClustering(n_clusters=2,affinity = 'euclidean', linkage = 'ward')
HC = cluster.fit_predict(X_principal)
HC
array([1, 0, 1, ..., 1, 0, 1], dtype=int64)
silhouette_score(X_principal,HC)
0.3999025735742288
from sklearn.cluster import DBSCAN
DBC = DBSCAN(eps=0.8,min_samples=15)
Label = DBC.fit_predict(X_principal)
np.unique(Label)
array([-1, 0], dtype=int64)
silhouette_score(X_principal,Label)
0.4745192062196644
plt.scatter(X_principal['P1'],X_principal['P2'],c= Label,s=60)
plt.title('DBSCAN Clusters')
plt.show()